#Optimize Delta Table Storage

[xaltius]

The top Data Engineering trends to look for in 2025

Data engineering is the unsung hero of our data-driven world. It's the critical discipline that builds and maintains the robust infrastructure enabling organizations to collect, store, process, and analyze vast amounts of data. As we navigate mid-2025, this foundational field is evolving at an unprecedented pace, driven by the exponential growth of data, the insatiable demand for real-time insights, and the transformative power of AI.

Staying ahead of these shifts is no longer optional; it's essential for data engineers and the organizations they support. Let's dive into the key data engineering trends that are defining the landscape in 2025.

1. The Dominance of the Data Lakehouse

What it is: The data lakehouse architecture continues its strong upward trajectory, aiming to unify the best features of data lakes (flexible, low-cost storage for raw, diverse data types) and data warehouses (structured data management, ACID transactions, and robust governance). Why it's significant: It offers a single platform for various analytics workloads, from BI and reporting to AI and machine learning, reducing data silos, complexity, and redundancy. Open table formats like Apache Iceberg, Delta Lake, and Hudi are pivotal in enabling lakehouse capabilities. Impact: Greater data accessibility, improved data quality and reliability for analytics, simplified data architecture, and cost efficiencies. Key Technologies: Databricks, Snowflake, Amazon S3, Azure Data Lake Storage, Apache Spark, and open table formats.

2. AI-Powered Data Engineering (Including Generative AI)

What it is: Artificial intelligence, and increasingly Generative AI, are becoming integral to data engineering itself. This involves using AI/ML to automate and optimize various data engineering tasks. Why it's significant: AI can significantly boost efficiency, reduce manual effort, improve data quality, and even help generate code for data pipelines or transformations. Impact: * Automated Data Integration & Transformation: AI tools can now automate aspects of data mapping, cleansing, and pipeline optimization. * Intelligent Data Quality & Anomaly Detection: ML algorithms can proactively identify and flag data quality issues or anomalies in pipelines. * Optimized Pipeline Performance: AI can help in tuning and optimizing the performance of data workflows. * Generative AI for Code & Documentation: LLMs are being used to assist in writing SQL queries, Python scripts for ETL, and auto-generating documentation. Key Technologies: AI-driven ETL/ELT tools, MLOps frameworks integrated with DataOps, platforms with built-in AI capabilities (e.g., Databricks AI Functions, AWS DMS with GenAI).

3. Real-Time Data Processing & Streaming Analytics as the Norm

What it is: The demand for immediate insights and actions based on live data streams continues to grow. Batch processing is no longer sufficient for many use cases. Why it's significant: Businesses across industries like e-commerce, finance, IoT, and logistics require real-time capabilities for fraud detection, personalized recommendations, operational monitoring, and instant decision-making. Impact: A shift towards streaming architectures, event-driven data pipelines, and tools that can handle high-throughput, low-latency data. Key Technologies: Apache Kafka, Apache Flink, Apache Spark Streaming, Apache Pulsar, cloud-native streaming services (e.g., Amazon Kinesis, Google Cloud Dataflow, Azure Stream Analytics), and real-time analytical databases.

4. The Rise of Data Mesh & Data Fabric Architectures

What it is: * Data Mesh: A decentralized sociotechnical approach that emphasizes domain-oriented data ownership, treating data as a product, self-serve data infrastructure, and federated computational governance. * Data Fabric: An architectural approach that automates data integration and delivery across disparate data sources, often using metadata and AI to provide a unified view and access to data regardless of where it resides. Why it's significant: Traditional centralized data architectures struggle with the scale and complexity of modern data. These approaches offer greater agility, scalability, and empower domain teams. Impact: Improved data accessibility and discoverability, faster time-to-insight for domain teams, reduced bottlenecks for central data teams, and better alignment of data with business domains. Key Technologies: Data catalogs, data virtualization tools, API-based data access, and platforms supporting decentralized data management.

5. Enhanced Focus on Data Observability & Governance

What it is: * Data Observability: Going beyond traditional monitoring to provide deep visibility into the health and state of data and data pipelines. It involves tracking data lineage, quality, freshness, schema changes, and distribution. * Data Governance by Design: Integrating robust data governance, security, and compliance practices directly into the data lifecycle and infrastructure from the outset, rather than as an afterthought. Why it's significant: As data volumes and complexity grow, ensuring data quality, reliability, and compliance (e.g., GDPR, CCPA) becomes paramount for building trust and making sound decisions. Regulatory landscapes, like the EU AI Act, are also making strong governance non-negotiable. Impact: Improved data trust and reliability, faster incident resolution, better compliance, and more secure data handling. Key Technologies: AI-powered data observability platforms, data cataloging tools with governance features, automated data quality frameworks, and tools supporting data lineage.

6. Maturation of DataOps and MLOps Practices

What it is: * DataOps: Applying Agile and DevOps principles (automation, collaboration, continuous integration/continuous delivery - CI/CD) to the entire data analytics lifecycle, from data ingestion to insight delivery. * MLOps: Extending DevOps principles specifically to the machine learning lifecycle, focusing on streamlining model development, deployment, monitoring, and retraining. Why it's significant: These practices are crucial for improving the speed, quality, reliability, and efficiency of data and machine learning pipelines. Impact: Faster delivery of data products and ML models, improved data quality, enhanced collaboration between data engineers, data scientists, and IT operations, and more reliable production systems. Key Technologies: Workflow orchestration tools (e.g., Apache Airflow, Kestra), CI/CD tools (e.g., Jenkins, GitLab CI), version control systems (Git), containerization (Docker, Kubernetes), and MLOps platforms (e.g., MLflow, Kubeflow, SageMaker, Azure ML).

The Cross-Cutting Theme: Cloud-Native and Cost Optimization

Underpinning many of these trends is the continued dominance of cloud-native data engineering. Cloud platforms (AWS, Azure, GCP) provide the scalable, flexible, and managed services that are essential for modern data infrastructure. Coupled with this is an increasing focus on cloud cost optimization (FinOps for data), as organizations strive to manage and reduce the expenses associated with large-scale data processing and storage in the cloud.

The Evolving Role of the Data Engineer

These trends are reshaping the role of the data engineer. Beyond building pipelines, data engineers in 2025 are increasingly becoming architects of more intelligent, automated, and governed data systems. Skills in AI/ML, cloud platforms, real-time processing, and distributed architectures are becoming even more crucial.

Global Relevance, Local Impact

These global data engineering trends are particularly critical for rapidly developing digital economies. In countries like India, where the data explosion is immense and the drive for digital transformation is strong, adopting these advanced data engineering practices is key to harnessing data for innovation, improving operational efficiency, and building competitive advantages on a global scale.

Conclusion: Building the Future, One Pipeline at a Time

The field of data engineering is more dynamic and critical than ever. The trends of 2025 point towards more automated, real-time, governed, and AI-augmented data infrastructures. For data engineering professionals and the organizations they serve, embracing these changes means not just keeping pace, but actively shaping the future of how data powers our world.

[kadellabs69]

Unlocking the Power of Delta Live Tables in Data bricks with Kadel Labs

Introduction

In the rapidly evolving landscape of big data and analytics, businesses are constantly seeking ways to streamline data processing, ensure data reliability, and improve real-time analytics. One of the most powerful solutions available today is Delta Live Tables (DLT) in Databricks. This cutting-edge feature simplifies data engineering and ensures efficiency in data pipelines.

Kadel Labs, a leader in digital transformation and data engineering solutions, leverages Delta Live Tables to optimize data workflows, ensuring businesses can harness the full potential of their data. In this article, we will explore what Delta Live Tables are, how they function in Databricks, and how Kadel Labs integrates this technology to drive innovation.

Understanding Delta Live Tables

What Are Delta Live Tables?

Delta Live Tables (DLT) is an advanced framework within Databricks that simplifies the process of building and maintaining reliable ETL (Extract, Transform, Load) pipelines. With DLT, data engineers can define incremental data processing pipelines using SQL or Python, ensuring efficient data ingestion, transformation, and management.

Key Features of Delta Live Tables

Automated Pipeline Management

DLT automatically tracks changes in source data, eliminating the need for manual intervention.

Data Reliability and Quality

Built-in data quality enforcement ensures data consistency and correctness.

Incremental Processing

Instead of processing entire datasets, DLT processes only new data, improving efficiency.

Integration with Delta Lake

DLT is built on Delta Lake, ensuring ACID transactions and versioned data storage.

Monitoring and Observability

With automatic lineage tracking, businesses gain better insights into data transformations.

How Delta Live Tables Work in Databricks

Databricks, a unified data analytics platform, integrates Delta Live Tables to streamline data lake house architectures. Using DLT, businesses can create declarative ETL pipelines that are easy to maintain and highly scalable.

The DLT Workflow

Define a Table and Pipeline

Data engineers specify data sources, transformation logic, and the target Delta table.

Data Ingestion and Transformation

DLT automatically ingests raw data and applies transformation logic in real-time.

Validation and Quality Checks

DLT enforces data quality rules, ensuring only clean and accurate data is processed.

Automatic Processing and Scaling

Databricks dynamically scales resources to handle varying data loads efficiently.

Continuous or Triggered Execution

DLT pipelines can run continuously or be triggered on-demand based on business needs.

Kadel Labs: Enhancing Data Pipelines with Delta Live Tables

As a digital transformation company, Kadel Labs specializes in deploying cutting-edge data engineering solutions that drive business intelligence and operational efficiency. The integration of Delta Live Tables in Databricks is a game-changer for organizations looking to automate, optimize, and scale their data operations.

How Kadel Labs Uses Delta Live Tables

Real-Time Data Streaming

Kadel Labs implements DLT-powered streaming pipelines for real-time analytics and decision-making.

Data Governance and Compliance

By leveraging DLT’s built-in monitoring and validation, Kadel Labs ensures regulatory compliance.

Optimized Data Warehousing

DLT enables businesses to build cost-effective data warehouses with improved data integrity.

Seamless Cloud Integration

Kadel Labs integrates DLT with cloud environments (AWS, Azure, GCP) to enhance scalability.

Business Intelligence and AI Readiness

DLT transforms raw data into structured datasets, fueling AI and ML models for predictive analytics.

Benefits of Using Delta Live Tables in Databricks

1. Simplified ETL Development

With DLT, data engineers spend less time managing complex ETL processes and more time focusing on insights.

2. Improved Data Accuracy and Consistency

DLT automatically enforces quality checks, reducing errors and ensuring data accuracy.

3. Increased Operational Efficiency

DLT pipelines self-optimize, reducing manual workload and infrastructure costs.

4. Scalability for Big Data

DLT seamlessly scales based on workload demands, making it ideal for high-volume data processing.

5. Better Insights with Lineage Tracking

Data lineage tracking in DLT provides full visibility into data transformations and dependencies.

Real-World Use Cases of Delta Live Tables with Kadel Labs

1. Retail Analytics and Customer Insights

Kadel Labs helps retailers use Delta Live Tables to analyze customer behavior, sales trends, and inventory forecasting.

2. Financial Fraud Detection

By implementing DLT-powered machine learning models, Kadel Labs helps financial institutions detect fraudulent transactions.

3. Healthcare Data Management

Kadel Labs leverages DLT in Databricks to improve patient data analysis, claims processing, and medical research.

4. IoT Data Processing

For smart devices and IoT applications, DLT enables real-time sensor data processing and predictive maintenance.

Conclusion

Delta Live Tables in Databricks is transforming the way businesses handle data ingestion, transformation, and analytics. By partnering with Kadel Labs, companies can leverage DLT to automate pipelines, improve data quality, and gain actionable insights.

With its expertise in data engineering, Kadel Labs empowers businesses to unlock the full potential of Databricks and Delta Live Tables, ensuring scalable, efficient, and reliable data solutions for the future.

For businesses looking to modernize their data architecture, now is the time to explore Delta Live Tables with Kadel Labs!

[tudipblog]

Medallion Architecture: A Scalable Framework for Modern Data Management

In the current big data era, companies must effectively manage data to make data-driven decisions. One such well-known data management architecture is the Medallion Architecture. This architecture offers a structured, scalable, modular approach to building data pipelines, ensuring data quality, and optimizing data operations.

What is Medallion Architecture?

Medallion Architecture is a system for managing and organizing data in stages. Each stage, or “medallion,” improves the quality and usefulness of the data, step by step. The main goal is to transform raw data into meaningful data that is ready for the analysis team.

The Three Layers of Medallion Architecture:

Bronze Layer (Raw Data):This layer stores all raw data exactly as it comes in without any changes or cleaning, preserving a copy of the original data for fixing errors or reprocessing when needed. Example: Logs from a website, sensor data, or files uploaded by users.

Silver Layer (Cleaned and Transformed Data):The Silver Layer involves cleaning, organizing, and validating data by fixing errors such as duplicates or missing values, ensuring the data is consistent and reliable for analysis, such as removing duplicate customer records or standardizing dates in a database Example: Removing duplicate customer records or standardizing dates in a database.

Gold Layer (Business-Ready Data):The Gold Layer contains final, polished data optimized for reports, dashboards, and decision-making, providing businesses with exactly the information they need to make informed decisions Example: A table showing the total monthly sales for each region

Advantages:

Improved Data Quality: Incremental layers progressively refine data quality from raw to business-ready datasets

Scalability: Each layer can be scaled independently based on specific business requirements

Security: If you have a large team to handle, you can separate them by their level

Modularity: The layered approach separates responsibilities, simplifying management and debugging

Traceability: Raw data preserved in the Bronze layer ensures traceability and allows reprocessing when issues arise in downstream layers

Adaptability: The architecture supports diverse data sources and formats, making it suitable for various business needs

Challenges:

Takes Time: Processing through multiple layers can delay results

Storage Costs: Storing raw and processed data requires more space

Requires Skills: Implementing this architecture requires skilled data engineers familiar with ETL/ELT tools, cloud platforms, and distributed systems

Best Practices for Medallion Architecture:

Automate ETL/ELT Processes: Use orchestration tools like Apache Airflow or AWS Step Functions to automate workflows between layers

Enforce Data Quality at Each Layer: Validate schemas, apply deduplication rules, and ensure data consistency as it transitions through layers

Monitor and Optimize Performance: Use monitoring tools to track pipeline performance and optimize transformations for scalability

Leverage Modern Tools: Adopt cloud-native technologies like Databricks, Delta Lake, or Snowflake to simplify the implementation

Plan for Governance: Implement robust data governance policies, including access control, data cataloging, and audit trails

Conclusion

Medallion Architecture is a robust framework for building reliable, scalable, and modular data pipelines. Its layered approach allows businesses to extract maximum value from their data by ensuring quality and consistency at every stage. While it comes with its challenges, the benefits of adopting Medallion Architecture often outweigh the drawbacks, making it a cornerstone for modern data engineering practices.

To learn more about this blog, please click on the link below: https://tudip.com/blog-post/medallion-architecture/.

[freshparadisepaper]

ATS-Friendly Engineering Resume Format: A Complete Guide

Introduction: Why Your Engineering Resume Must Beat the Bots

In today’s competitive job market, your resume must impress two audiences: robots and humans. Before a hiring manager even sees your application, Applicant Tracking Systems (ATS) scan it for relevance using keywords, formatting, and structure.

Here’s the deal: 75% of resumes are rejected by ATS before they reach a recruiter, according to Jobscan. So, if you’re an engineer looking to land that dream job, your resume needs to be ATS-optimized—without sacrificing readability or personal flair.

This complete guide dives into:

What ATS is and how it works

The best resume structure for engineers

Actionable formatting tips

Real-world engineering resume examples that pass ATS filters

Pro tips to stand out in technical roles

📘 What Is an ATS and Why Should Engineers Care?

An Applicant Tracking System is software used by companies to filter, organize, and score resumes. It looks for specific keywords, skills, and sections to determine if you're a good match for the role.

If your resume isn't formatted properly or lacks relevant terminology, the ATS may automatically discard it—even if you're the perfect candidate.

🧰 Key Elements of an ATS-Friendly Engineering Resume

To ensure your resume gets past ATS and into the hands of a hiring manager, focus on these elements:

✅ 1. Use a Standard Format

Avoid creative or overly designed templates. ATS prefers clean, chronological or hybrid formats.

Use standard section headings: Experience, Education, Skills, Projects

Stick to widely accepted file types: PDF or DOCX

Avoid headers/footers, tables, and graphics

✅ 2. Include Relevant Keywords

Tailor your resume to match the job description by using relevant industry terms. This is where many strong candidates fall short.

For example:

Instead of “Worked with machines,” say “Operated and maintained CNC machinery to meet production targets.”

✅ 3. Keep It Simple

Use standard fonts like Arial, Calibri, or Times New Roman

Stick to black text and avoid icons or images

Keep bullet points concise and achievement-focused

✅ 4. Professional Summary vs Objective

Include a Professional Summary at the top rather than an outdated objective. In just 2–3 lines, explain who you are and the value you bring.

Example:

Detail-oriented Mechanical Engineer with 5+ years of experience designing and testing HVAC systems. Proficient in AutoCAD and SolidWorks, with a passion for energy-efficient innovation.

🔍 Engineering Resume Examples (That Work)

💡 Software Engineer (Entry-Level)

plaintext

CopyEdit

Experience: Intern – ABC Tech Solutions | June 2024 – August 2024 - Collaborated with a team of 5 developers to build a REST API for a cloud storage platform. - Debugged and resolved over 20 software issues using Python and Git. Skills: Python, JavaScript, REST APIs, Git, Agile

💡 Mechanical Engineer (Mid-Level)

plaintext

CopyEdit

Experience: Mechanical Engineer – Delta Industrial | Jan 2020 – Present - Led a team of 4 in redesigning conveyor systems, reducing breakdowns by 40%. - Utilized AutoCAD and MATLAB to improve component designs, saving $50K annually. Skills: AutoCAD, MATLAB, SolidWorks, FEA Analysis, Six Sigma

💡 Civil Engineer (Senior-Level)

plaintext

CopyEdit

Experience: Senior Civil Engineer – UrbanTech Projects | March 2018 – Present - Managed infrastructure projects worth $10M+, ensuring completion under budget and ahead of schedule. - Coordinated with cross-functional teams to conduct environmental and zoning assessments. Skills: Structural Design, Project Management, AutoCAD Civil 3D, OSHA Standards

These engineering resume examples demonstrate clear formatting, keyword optimization, and quantifiable achievements—all essential for ATS success.

📏 Engineering Resume Format: The Ideal Structure

Here’s the best format engineers should follow for ATS compatibility:

plaintext

CopyEdit

1. Name & Contact Information 2. Professional Summary 3. Core Skills / Technical Skills 4. Professional Experience (Reverse Chronological) 5. Education 6. Certifications / Licenses (if applicable) 7. Projects (Optional for students and tech roles)

💡 Tip: Make sure job titles, company names, and dates are left-aligned and consistent. Use bullet points for achievements—not responsibilities.

📈 Stats That Prove ATS Optimization Matters

98.8% of Fortune 500 companies use ATS software (Jobscan)

Job seekers who customize their resumes to each job description are 3x more likely to get interviews (CareerBuilder)

On average, recruiters spend just 7.4 seconds scanning a resume (Ladders, Inc.)

🧠 Pro Tips to Maximize ATS Compatibility

Match your resume to each job post using keywords from the job description

Avoid abbreviations unless they're industry standard (e.g., use "Bachelor of Science in Electrical Engineering" not just "BSEE")

Use metrics to show value (e.g., “Improved throughput by 30%”)

Skip columns, text boxes, and fancy design elements

💬 Conclusion: Beat the Bots, Impress the Humans

Writing an engineering resume that’s ATS-friendly doesn’t mean you need to sacrifice personality or impact. It simply means making your content accessible to both AI filters and human recruiters.

Whether you’re a student creating your first resume or a seasoned engineer applying for a leadership role, these strategies—and the engineering resume examples above—will help your application rise to the top of the pile.

[tudip123]

Medallion Architecture: A Scalable Framework for Modern Data Management

In the current big data era, companies must effectively manage data to make data-driven decisions. One such well-known data management architecture is the Medallion Architecture. This architecture offers a structured, scalable, modular approach to building data pipelines, ensuring data quality, and optimizing data operations.

What is Medallion Architecture?

Medallion Architecture is a system for managing and organizing data in stages. Each stage, or “medallion,” improves the quality and usefulness of the data, step by step. The main goal is to transform raw data into meaningful data that is ready for the analysis team.

The Three Layers of Medallion Architecture:

Bronze Layer (Raw Data):This layer stores all raw data exactly as it comes in without any changes or cleaning, preserving a copy of the original data for fixing errors or reprocessing when needed. Example: Logs from a website, sensor data, or files uploaded by users.

Silver Layer (Cleaned and Transformed Data):The Silver Layer involves cleaning, organizing, and validating data by fixing errors such as duplicates or missing values, ensuring the data is consistent and reliable for analysis, such as removing duplicate customer records or standardizing dates in a database Example: Removing duplicate customer records or standardizing dates in a database.

Gold Layer (Business-Ready Data):The Gold Layer contains final, polished data optimized for reports, dashboards, and decision-making, providing businesses with exactly the information they need to make informed decisions Example: A table showing the total monthly sales for each region

Advantages:

Improved Data Quality: Incremental layers progressively refine data quality from raw to business-ready datasets

Scalability: Each layer can be scaled independently based on specific business requirements

Security: If you have a large team to handle, you can separate them by their level

Modularity: The layered approach separates responsibilities, simplifying management and debugging

Traceability: Raw data preserved in the Bronze layer ensures traceability and allows reprocessing when issues arise in downstream layers

Adaptability: The architecture supports diverse data sources and formats, making it suitable for various business needs

Challenges:

Takes Time: Processing through multiple layers can delay results

Storage Costs: Storing raw and processed data requires more space

Requires Skills: Implementing this architecture requires skilled data engineers familiar with ETL/ELT tools, cloud platforms, and distributed systems

Best Practices for Medallion Architecture:

Automate ETL/ELT Processes: Use orchestration tools like Apache Airflow or AWS Step Functions to automate workflows between layers

Enforce Data Quality at Each Layer: Validate schemas, apply deduplication rules, and ensure data consistency as it transitions through layers

Monitor and Optimize Performance: Use monitoring tools to track pipeline performance and optimize transformations for scalability

Leverage Modern Tools: Adopt cloud-native technologies like Databricks, Delta Lake, or Snowflake to simplify the implementation

Plan for Governance: Implement robust data governance policies, including access control, data cataloging, and audit trails

Conclusion

Medallion Architecture is a robust framework for building reliable, scalable, and modular data pipelines. Its layered approach allows businesses to extract maximum value from their data by ensuring quality and consistency at every stage. While it comes with its challenges, the benefits of adopting Medallion Architecture often outweigh the drawbacks, making it a cornerstone for modern data engineering practices.

Click the link below to learn more about Medallion Architecture:

[pythonjobsupport]

Learn Delta Lake in One Video in 30 Mins| Full Tutorial | Databricks |

What is delta table? Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the … source

[marketindustrydevopment]

Global Battery Management System Market Research Report Released With Growth, Latest Trends & Forecasts Till 2030

Battery Management System Market Overview and Insights:

According to Introspective Market Research, Battery Management System Market Size Was Valued at USD 8.40 Billion in 2022, and is Projected to Reach USD 12.60 Billion by 2030, Growing at a CAGR of 5.2% From 2023-2030.

A Battery Management System (BMS) is an electronic system that manages and monitors the performance of rechargeable batteries, typically used in applications ranging from consumer electronics to electric vehicles (EVs) and renewable energy storage systems. BMS supports remote monitoring and control capabilities, enabling users to access battery performance data, receive alerts, and adjust settings remotely via connected devices or network interfaces. Battery Management Systems are essential components in ensuring the safe and efficient operation of rechargeable battery packs across various applications, playing a critical role in optimizing performance, prolonging battery life, and enhancing overall system reliability.

The Global Battery Management System Market study contains information on the global industry, as well as user data and numbers. The Global Market is examined in depth in this research report, including raw material suppliers, industry chain structures, and manufacturing. The Battery Management System Sales market investigates the market's most important segments. This insightful analysis includes historical data as well as a predicted timeframe. This report examines the whole value chain, as well as downstream and upstream fundamentals. This Market study examines the Battery Management System Industry's technical data, production plants, and raw material suppliers, as well as which product has the largest penetration, profit margins, and R&D status.

Who are the key players operating in the industry?

Storage Battery Systems LLC (United States),Elithion Inc. (United States),Cummins Inc. (United States),Navitas Systems (United States),Valence Technology, Inc. (United States),Texas Instruments (United States),Analog Devices, Inc. (United States),Eberspächer (Germany),Leclanché SA (Switzerland),Lithium Balance A/S (Denmark),Panasonic Holdings Corporation (Japan),LG Energy Solution, Ltd. (South Korea),Delta Electronics (Taiwan),Contemporary Amperex Technology Co., Limited (CATL) (China),BYD Company Ltd. (China),Contemporary Amperex Technology Co., Ltd. (China),Guoxuan Hi-Tech Co., Ltd. (China),Sunwoda Electronic Co., Ltd. (China),CALB (China),Tianjin Lishen Battery Joint-Stock Co., Ltd. (China),Starway Battery (China),SVOLT Energy Technology Co., Ltd. (China) And Other Major Players.

Battery Management System Market research is an ongoing process. Regularly monitor and evaluate market dynamics to stay informed and adapt your strategies accordingly. As market research and consulting firm we offer market research report which is focusing on major parameters including Target Market Identification, Customer Needs and Preferences, Thorough Competitor Analysis, Market Size & Market Analysis, and other major factors. At the end we do provide meaningful insights and actionable recommendations that inform decision-making and strategy development.

Get Sample PDF of Battery Management System Market with Complete TOC, Tables & Figures @

https://introspectivemarketresearch.com/request/16754

What is included in Battery Management System market segmentation?

The report has segmented the market into the following categories:

By Type

Motive Battery

Stationary Battery

By Battery Type

Lithium-ion

Lead-acid

Nickel-based

Solid-state

Flow battery

By Topology

Centralized

Distributed

Modular

By Region:

North America (U.S., Canada, Mexico)

Eastern Europe (Bulgaria, The Czech Republic, Hungary, Poland, Romania, Rest of Eastern Europe)

Western Europe (Germany, U.K., France, Netherlands, Italy, Russia, Spain, Rest of Western Europe)

Asia-Pacific (China, India, Japan, South Korea, Malaysia, Thailand, Vietnam, The Philippines, Australia, New Zealand, Rest of APAC)

Middle East & Africa (Turkey, Saudi Arabia, Bahrain, Kuwait, Qatar, UAE, Israel, South Africa)

Why Choose Introspective Market Research Report?

A smart dashboard that provides updated details on industry trends.

Data input from various network entities such as suppliers, suppliers, service providers etc.

Strict quality inspection standards: data collection, triangulation and verification.

We provide service 24 hours a day, 365 days a year.

Competitive Analysis of the market in the report identifies various key manufacturers of the market. We do company profiling for major key players. The research report includes Competitive Positioning, Investment Analysis, BCG Matrix, Heat Map Analysis, and Mergers & Acquisitions. It helps the reader understand the strategies and collaborations that players are targeting to combat competition in the market. The comprehensive report offers a significant microscopic look at the market. The reader can identify the footprints of the manufacturers by knowing about the product portfolio, the global price of manufacturers, and production by producers during the forecast period.

Discount on the Research Report @

https://introspectivemarketresearch.com/discount/16754

Research Methodology:

Introspective Market Research inculcated modern methodologies to obtain, summarize and analyze authentic data to produce a highly relevant report which helps to make sound decision making. Primarily, we are working based on research methodologies, including primary and secondary research.

By Region:

North America (U.S., Canada, Mexico)

Eastern Europe (Bulgaria, The Czech Republic, Hungary, Poland, Romania, Rest of Eastern Europe)

Western Europe (Germany, U.K., France, Netherlands, Italy, Russia, Spain, Rest of Western Europe)

Asia-Pacific (China, India, Japan, South Korea, Malaysia, Thailand, Vietnam, The Philippines, Australia, New Zealand, Rest of APAC)

Middle East & Africa (Turkey, Saudi Arabia, Bahrain, Kuwait, Qatar, UAE, Israel, South Africa)

Highlights from the report:

Market Study: It includes key market segments, key manufacturers covered, product range offered in the years considered, Global Battery Management System Market, and research objectives. It also covers segmentation study provided in the report based on product type and application.

Market Executive Summary: This section highlights key studies, market growth rates, competitive landscape, market drivers, trends, and issues in addition to macro indicators.

Market Production by Region: The report provides data related to imports and exports, revenue, production and key players of all the studied regional markets are covered in this section.

Battery Management System Market Profiles of Top Key Competitors: Analysis of each profiled Roll Hardness Tester market player is detailed in this section. This segment also provides SWOT analysis of individual players, products, production, value, capacity, and other important factors.

If you require any specific information that is not covered currently within the scope of the report, we will provide the same as a part of the customization.

About us:

Introspective Market Research (introspectivemarketresearch.com) is a visionary research consulting firm dedicated to assist our clients grow and have a successful impact on the market. Our team at IMR is ready to assist our clients flourish their business by offering strategies to gain success and monopoly in their respective fields. We are a global market research company, specialized in using big data and advanced analytics to show the bigger picture of the market trends. We help our clients to think differently and build better tomorrow for all of us. We are a technology-driven research company, we analyze extremely large sets of data to discover deeper insights and provide conclusive consulting. We not only provide intelligence solutions, but we help our clients in how they can achieve their goals.

Contact us:

Introspective Market Research

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Ph no: +1 773 382 1049

Email : sales@introspectivemarketresearch.com

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10 Reasons to Make Apache Iceberg and Dremio Part of Your Data Lakehouse Strategy

Get a Free Copy of "Apache Iceberg: The Definitive Guide" Build an Iceberg Lakehouse on Your Laptop

Apache Iceberg is disrupting the data landscape, offering a new paradigm where data is not confined to the storage system of a chosen data warehouse vendor. Instead, it resides in your own storage, accessible by multiple tools. A data lakehouse, which is essentially a modular data warehouse built on your data lake as the storage layer, offers limitless configuration possibilities. Among the various options for constructing an Apache Iceberg lakehouse, the Dremio Data Lakehouse Platform stands out as one of the most straightforward, rapid, and cost-effective choices. This platform has gained popularity for on-premises migrations, implementing data mesh strategies, enhancing BI dashboards, and more. In this article, we will explore 10 reasons why the combination of Apache Iceberg and the Dremio platform is exceptionally powerful. We will delve into five reasons to choose Apache Iceberg over other table formats and five reasons to opt for the Dremio platform when considering Semantic Layers, Query Engines, and Lakehouse Management.

5 Reasons to Choose Apache Iceberg Over Other Table Formats

Apache Iceberg is not the only table format available; Delta Lake and Apache Hudi are also key players in this domain. All three formats provide a core set of features, enabling database-like tables on your data lake with capabilities such as ACID transactions, time-travel, and schema evolution. However, there are several unique aspects that make Apache Iceberg a noteworthy option to consider.

1. Partition Evolution

Apache Iceberg distinguishes itself with a feature known as partition evolution, which allows users to modify their partitioning scheme at any time without the need to rewrite the entire table. This capability is unique to Iceberg and carries significant implications, particularly for tables at the petabyte scale where altering partitioning can be a complex and costly process. Partition evolution facilitates the optimization of data management, as it enables users to easily revert any changes to the partitioning scheme by simply rolling back to a previous snapshot of the table. This flexibility is a considerable advantage in managing large-scale data efficiently.

2. Hidden Partitioning

Apache Iceberg introduces a unique feature called hidden partitioning, which significantly simplifies the workflows for both data engineers and data analysts. In traditional partitioning approaches, data engineers often need to create additional partitioning columns derived from existing ones, which not only increases storage requirements but also complicates the data ingestion process. Additionally, data analysts must be cognizant of these extra columns; failing to filter by the partition column could lead to a full table scan, undermining efficiency.

However, with Apache Iceberg's hidden partitioning, the system can partition tables based on the transformed value of a column, with this transformation tracked in the metadata, eliminating the need for physical partitioning columns in the data files. This means that analysts can apply filters directly on the original columns and still benefit from the optimized performance of partitioning. This feature streamlines operations for both data engineers and analysts, making the process more efficient and less prone to error.

3. Versioning

Versioning is an invaluable feature that facilitates isolating changes, executing rollbacks, simultaneously publishing numerous changes across different objects, and creating zero-copy environments for experimentation and development. While each table format records a single chain of changes, allowing for rollbacks, Apache Iceberg uniquely incorporates branching, tagging, and merging as integral aspects of its core table format. Furthermore, Apache Iceberg stands out as the sole format presently compatible with Nessie, an open-source project that extends versioning capabilities to include commits, branches, tags, and merges at the multi-table catalog level, thereby unlocking a plethora of new possibilities.

These advanced versioning features in Apache Iceberg are accessible through ergonomic SQL interfaces, making them user-friendly and easily integrated into data workflows. In contrast, other formats typically rely on file-level versioning, which necessitates the use of command-line interfaces (CLIs) and imperative programming for management, making them less approachable and more cumbersome to use. This distinction underscores Apache Iceberg's advanced capabilities and its potential to significantly enhance data management practices.

4. Lakehouse Management

Apache Iceberg is attracting a growing roster of vendors eager to assist in managing tables, offering services such as compaction, sorting, snapshot cleanup, and more. This support makes using Iceberg tables as straightforward as utilizing tables in a traditional database or data warehouse. In contrast, other table formats typically rely on a single tool or vendor for data management, which can lead to vendor lock-in. With Iceberg, however, there is a diverse array of vendors, including Dremio, Tabular, Upsolver, AWS, and Snowflake, each providing varying levels of table management features. This variety gives users the flexibility to choose a vendor that best fits their needs, enhancing Iceberg's appeal as a versatile and user-friendly data management solutio

5. Open Culture

One of the most persuasive arguments for adopting Apache Iceberg is its dynamic open-source culture, which permeates its development and ecosystem. Development discussions take place on publicly accessible mailing lists and emails, enabling anyone to participate in and influence the format's evolution. The ecosystem is expanding daily, with an increasing number of tools offering both read and write support, reflecting the growing enthusiasm among vendors. This open environment provides vendors with the confidence to invest their resources in supporting Iceberg, knowing they are not at the mercy of a single vendor who could unpredictably alter or restrict access to the format. This level of transparency and inclusivity not only fosters innovation and collaboration but also ensures a level of stability and predictability that is highly valued in the tech industry.

Dremio

Dremio is a comprehensive data lakehouse platform that consolidates numerous functionalities, typically offered by different vendors, into a single solution. It unifies data analytics through data virtualization and a semantic layer, streamlining the integration and interpretation of data from diverse sources. Dremio's robust SQL query engine is capable of federating queries across various data sources, offering transparent acceleration to enhance performance. Additionally, Dremio's suite of lakehouse management features includes a Nessie-powered data catalog, which ensures data is versioned and easily transportable, alongside automated table maintenance capabilities. This integration of multiple key features into one platform simplifies the data management process, making Dremio a powerful and efficient tool for organizations looking to harness the full potential of their data lakehouse.

5. Apache Arrow

One of the key reasons Dremio's SQL query engine outperforms other distributed query engines and data warehouses is its core reliance on Apache Arrow, an in-memory data format increasingly recognized as the de facto standard for analytical processing. Apache Arrow facilitates the swift and efficient loading of data from various sources into a unified format optimized for speedy processing. Moreover, it introduces a transport protocol known as Apache Arrow Flight, which significantly reduces serialization/deserialization bottlenecks often encountered when transferring data over the network within a distributed system or between different systems. This integration of Apache Arrow at the heart of Dremio's architecture enhances its query performance, making it a powerful tool for data analytics.

4. Columnar Cloud Cache

One common bottleneck in querying a data lake based on object storage is the latency experienced when retrieving a large number of objects from storage. Additionally, each individual request can incur a cost, contributing to the overall storage access expenses. Dremio addresses these challenges with its C3 (Columnar Cloud Cache) feature, which caches frequently accessed data on the NVMe memory of nodes within the Dremio cluster. This caching mechanism enables rapid access to data during subsequent query executions that require the same information. As a result, the more queries that are run, the more efficient Dremio becomes. This not only enhances query performance over time but also reduces costs, making Dremio an increasingly cost-effective and faster solution as usage grows. This anti-fragile nature of Dremio, where it strengthens and improves with stress or demand, is a significant advantage for organizations looking to optimize their data querying capabilities.

3. Reflections

Other engines often rely on materialized views and BI extracts to accelerate queries, which can require significant manual effort to maintain. This process creates a broader array of objects that data analysts must track and understand when to use. Moreover, many platforms cannot offer this acceleration across all their compatible data sources.

In contrast, Dremio introduces a unique feature called Reflections, which simplifies query acceleration without adding to the management workload of engineers or expanding the number of namespaces analysts need to be aware of. Reflections can be applied to any table or view within Dremio, allowing for the materialization of rows or the aggregation of calculations on the dataset.

For data engineers, Dremio automates the management of these materializations, treating them as Iceberg tables that can be intelligently substituted when a query that would benefit from them is detected. Data analysts, on the other hand, continue to query tables and build dashboards as usual, without needing to navigate additional namespaces. They will, however, experience noticeable performance improvements immediately, without any extra effort. This streamlined approach not only enhances efficiency but also significantly reduces the complexity typically associated with optimizing query performance.

2. Semantic Layer

Many query engines and data warehouses lack the capability to offer an organized, user-friendly interface for end users, a feature known as a semantic layer. This layer is crucial for providing logical, intuitive views for understanding and discovering data. Without this feature, organizations often find themselves needing to integrate services from additional vendors, which can introduce a complex web of dependencies and potential conflicts to manage.

Dremio stands out by incorporating an easy-to-use semantic layer within its lakehouse platform. This feature allows users to organize and document data from all sources into a single, coherent layer, facilitating data discovery. Beyond organization, Dremio enables robust data governance through role-based, column-based, and row-based access controls, ensuring users can only access the data they are permitted to view.

This semantic layer enhances collaboration across data teams, offering a unified access point that supports the implementation of data-centric architectures like data mesh. By streamlining data access and collaboration, Dremio not only makes data more discoverable and understandable but also ensures a secure and controlled data environment, aligning with best practices in data management and governance.

5. Hybrid Architecture

Many contemporary data tools focus predominantly on cloud-based data, sidelining the vast reserves of on-premise data that cannot leverage these modern solutions. Dremio, however, stands out by offering the capability to access on-premise data sources in addition to cloud data. This flexibility allows Dremio to unify on-premise and cloud data sources, facilitating seamless migrations between different systems. With Dremio, organizations can enhance their on-premise data by integrating it with the wealth of data available in cloud data marketplaces, all without the need for data movement. This approach not only broadens the scope of data resources available to businesses but also enables a more integrated and comprehensive data strategy, accommodating the needs of organizations with diverse data environments.

Conclusion

Apache Iceberg and Dremio are spearheading a transformative shift in data management and analysis. Apache Iceberg's innovative features, such as partition evolution, hidden partitioning, advanced versioning, and an open-source culture, set it apart in the realm of data table formats, offering flexibility, efficiency, and a collaborative development environment. On the other hand, Dremio's data lakehouse platform leverages these strengths and further enhances the data management experience with its integrated SQL query engine, semantic layer, and unique features like Reflections and the C3 Columnar Cloud Cache.

By providing a unified platform that addresses the challenges of both on-premise and cloud data, Dremio eliminates the complexity and fragmentation often associated with data analytics. Its ability to streamline data processing, ensure robust data governance, and facilitate seamless integration across diverse data sources makes it an invaluable asset for organizations aiming to leverage their data for insightful analytics and informed decision-making.

Together, Apache Iceberg and Dremio not only offer a robust foundation for data management but also embody the future of data analytics, where accessibility, efficiency, and collaboration are key. Whether you're a data engineer looking to optimize data storage and retrieval or a data analyst seeking intuitive and powerful data exploration tools, this combination presents a compelling solution in the ever-evolving landscape of data technology.

Get a Free Copy of "Apache Iceberg: The Definitive Guide" Build an Iceberg Lakehouse on Your Laptop

[govindhtech]

Dominate Structured and Semi-Structured Data Explosion

Semi-structured data like JSON

Increased data generation in enterprises includes structured transactional data, semi-structured data like JSON, and unstructured data like images and audio. Unlock the power of semi-structured data with BigQuery’s JSON Type. Data processing, storage, and query engines must build custom transformation pipelines to handlesemi-structured data and unstructured data due to its diversity and volume.

This post will discuss BigQuery‘s architectural concepts forsemi-structured data JSON, which eliminates complex preprocessing and provides schema flexibility, intuitive querying, and structured data’s scalability. They will discuss storage format optimizations, architecture performance benefits, and how they affect JSON path billing.

Capacitor File Format integration

BigQuery’s storage architecture relies on columnar capacitor storage. This format stores exabytes of data and serves millions of queries after a decade of research and optimization. The capacitor is designed for structured data. Dictionary, RLE, Delta encoding, and others help Capacitor store column values optimally. It also reorders records to maximize RLE. Capacitor can permute rows to improve RLE effectiveness since table row order rarely matters. An embedded expression library uses columnar storage for block-oriented vectorized processing.

They created the next-generation BigQuery Capacitor format for sparse semi-structured data to natively support JSON.

JSON is shredded into virtual columns as much as possible during ingestion. Most JSON keys are written once per column, not per row. Column data excludes colons and whitespace. Putting values in columns lets us use semi-structured data encodings like Dictionary, Run Length, and Delta. This greatly reduces query-time storage and IO costs. The format natively understands JSON nulls and arrays, optimizing virtual column storage.

BigQuery’s native JSON data type understands JSON objects, arrays, scalar types, nulls (‘null’), and empty arrays to preserve its nested structure.

Capacitor, the native JSON data type’s file-format, reorders records to group similar data and types. Record-reordering maximizes Run Length Encoding across rows to reduce virtual columns. For a key with integer and string values across a range of rows in the file, record-reordering groups the rows with the string data type and the rows with the integer data type, resulting in run length encoded spans of missing values in both virtual columns and smaller columns.

Capacitor is optimized for structured datasets. This was difficult for JSON data, which has many shapes and types. They overcame these challenges while building the next-generation Capacitor that natively supported JSON.

Add/Remove keys

As optional elements, JSON keys are marked as missing in rows without them.

Scalar Type Change

Virtual columns store keys that change scalar types like string, int, bool, and float across rows.

Non-scalar type changes

Non-scalar values like object and array are stored in an optimized binary format for parsing.

After shredding JSON data into virtual columns, the logical size of each column is calculated based on data size at ingestion.

Better query performance with JSON native data types

You would have to load the entire JSON STRING row from storage, decompress it, and evaluate each filter and projection expression one row at a time to filter or project specific JSON paths.

Unlike native JSON, BigQuery processes only the necessary virtual columns. They added compute and filter pushdown of JSON operations to improve projection and filter efficiency. Pushing projections and filter operations down to the embedded evaluation layer allows vectorized operations over virtual columns, making them more efficient than STRING type.

Customers are only charged for the size of the virtual columns scanned to return the JSON paths requested in the SQL query. In the query below, only the virtual columns representing the JSON keys `reference_id` and `id` are scanned across the data if payload is a JSON column with those keys.

On-demand billing for JSON shows the number of logical bytes scanned. Each virtual column has a native data type (INT64, FLOAT, STRING, BOOL), so the data size calculation is a sum of the scanned `reference_id` and `id` sizes, following the standard bigquery data type size.

Optimization virtual columns allow BigQuery Editions queries to use less IO and CPU than storing the JSON string unchanged because you’re scanning specific columns instead of loading the entire blob and extracting the paths.

BigQuery can now process only the SQL query-requested JSON paths with the new type. This can significantly lower query costs.

Read more on Govindhtech.com

[myhouseidea]

The Long Beach House is a project designed by Ward 5 Design. This contemporary beach front three-story new build construction house in Long Beach NY serves as a year round communal holiday home for two families. Photography by Pixy Lau.

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.

The layout of this house is narrow, and tall with 4 bedrooms and 5 bathrooms therefore every inch of the interior had to be maximized for space, and optimal use. To achieve this goal, nearly every piece of furniture in the residence was custom designed and built.

Because this house sits on the ocean, we wanted to connect its residents with the beach, even when they were indoors. For this reason, we chose a light color palette of pale blue and beige while infusing stark black, and pops of color often found near the beach. We used subtle infusions of color on top floor ceiling by painting it with a very light blue. This gives the feeling of being outdoors, even when you’re sitting in the living room.

The entryway is fitted with custom-built white oak cabinetry, slate tile flooring, and a bright cerulean blue cotton rug to add bold casual style. One of the most impressive feats of this house is the passenger elevator, that carries people between all floors.

The second floor features one of two master suites which was decorated with dark teal wallpaper and a rug which mimics the ocean floor. Two children’s bedrooms can support sleepovers with groups of friends and include custom suspended loft bunk beds with storage. As opposed to the traditional, bulky nature of bunk beds, these small rooms needed to accommodate groups of kids, without closing the room off. Fit to the styles of the kids (boys and girls) one room has feminine touches, such as the pink and grey pallet, while the other displays a black and blue theme.

The top level has some of the most notable designs in the home with a large open kitchen equipped with a wall of continuous white cabinetry, surrounding a gray quartzite-topped island. Black sofas sit atop a metallic blue hyde rug, beside painted driftwood side tables. The room is flooded with natural light, making this house the perfect year round sanctuary.

The second master suite, beautifully embellished with custom furnishings such as an oak accent wall, cabinets, nightstands, and storage bed were all made uniquely for the room.

The master bathrooms were designed with a black and white palette in mind and feature white penny round tiled showers with terrazzo floors. The powder room was custom painted as a colorful surprise for guests who come to visit.

Overall, this home is the perfect balance between beachy island decor, and modern sophistication.

Furnishings: Kitchen/Living Room: Sofas – Century Furniture, Dining Table – Ethnicraft, Dining Chairs – Design Within Reach Charles Eames, Rug – Saddalmans

Bathrooms: Fixtures – Delta, Tile – Ann Sacks

Master Suite 1: Wallpaper – York Grasscloth, Rug – Stark Carpet, Wall Hanging – Custom

The Long Beach House by Ward 5 Design The Long Beach House is a project designed by Ward 5 Design. This contemporary beach front three-story new build construction house in Long Beach NY serves as a year round communal holiday home for two families.

[azuredata]

Best Azure Data Engineer Course In Ameerpet | Azure Data

Understanding Delta Lake in Databricks

Introduction

Delta Lake, an open-source storage layer developed by Databricks, is designed to address these challenges. It enhances Apache Spark's capabilities by providing ACID transactions, schema enforcement, and time travel, making data lakes more reliable and efficient. In modern data engineering, managing large volumes of data efficiently while ensuring reliability and performance is a key challenge.

What is Delta Lake?

Delta Lake is an optimized storage layer built on Apache Parquet that brings the reliability of a data warehouse to big data processing. It eliminates the limitations of traditional data lakes by adding ACID transactions, scalable metadata handling, and schema evolution. Delta Lake integrates seamlessly with Azure Databricks, Apache Spark, and other cloud-based data solutions, making it a preferred choice for modern data engineering pipelines. Microsoft Azure Data Engineer

Key Features of Delta Lake

1. ACID Transactions

One of the biggest challenges in traditional data lakes is data inconsistency due to concurrent read/write operations. Delta Lake supports ACID (Atomicity, Consistency, Isolation, Durability) transactions, ensuring reliable data updates without corruption. It uses Optimistic Concurrency Control (OCC) to handle multiple transactions simultaneously.

2. Schema Evolution and Enforcement

Delta Lake enforces schema validation to prevent accidental data corruption. If a schema mismatch occurs, Delta Lake will reject the data, ensuring consistency. Additionally, it supports schema evolution, allowing modifications without affecting existing data.

3. Time Travel and Data Versioning

Delta Lake maintains historical versions of data using log-based versioning. This allows users to perform time travel queries, enabling them to revert to previous states of data. This is particularly useful for auditing, rollback, and debugging purposes. Azure Data Engineer Course

4. Scalable Metadata Handling

Traditional data lakes struggle with metadata scalability, especially when handling billions of files. Delta Lake optimizes metadata storage and retrieval, making queries faster and more efficient.

5. Performance Optimizations (Data Skipping and Caching)

Delta Lake improves query performance through data skipping and caching mechanisms. Data skipping allows queries to read only relevant data instead of scanning the entire dataset, reducing processing time. Caching improves speed by storing frequently accessed data in memory.

6. Unified Batch and Streaming Processing

Delta Lake enables seamless integration of batch and real-time streaming workloads. Structured Streaming in Spark can write and read from Delta tables in real-time, ensuring low-latency updates and enabling use cases such as fraud detection and log analytics.

How Delta Lake Works in Databricks?

Delta Lake is tightly integrated with Azure Databricks and Apache Spark, making it easy to use within data pipelines. Below is a basic workflow of how Delta Lake operates: Azure Data Engineering Certification

Data Ingestion: Data is ingested into Delta tables from multiple sources (Kafka, Event Hubs, Blob Storage, etc.).

Data Processing: Spark SQL and PySpark process the data, applying transformations and aggregations.

Data Storage: Processed data is stored in Delta format with ACID compliance.

Query and Analysis: Users can query Delta tables using SQL or Spark.

Version Control & Time Travel: Previous data versions are accessible for rollback and auditing.

Use Cases of Delta Lake

ETL Pipelines: Ensures data reliability with schema validation and ACID transactions.

Machine Learning: Maintains clean and structured historical data for training ML models. Azure Data Engineer Training

Real-time Analytics: Supports streaming data processing for real-time insights.

Data Governance & Compliance: Enables auditing and rollback for regulatory requirements.

Conclusion

Delta Lake in Databricks bridges the gap between traditional data lakes and modern data warehousing solutions by providing reliability, scalability, and performance improvements. With ACID transactions, schema enforcement, time travel, and optimized query performance, Delta Lake is a powerful tool for building efficient and resilient data pipelines. Its seamless integration with Azure Databricks and Apache Spark makes it a preferred choice for data engineers aiming to create high-performance and scalable data architectures.

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[matthew98sblog]

Business Data Replication from SAP to Google BigQuery

Are you looking to seamlessly query data through virtual tables directly by using hyper-scale storage? The best and the most optimized method is to integrate business data present in the SAP Warehouse Cloud with Google BigQuery.

There are several benefits of replicating data from SAP to BigQuery. The most critical one is the ability to analyze SAP and various third-party data in a single location. This leads to maximizing ROI both in SAP and Google services. Another important benefit is live analytics without needing to replicate data. This ensures that real-time connectivity is established to check BigQuery through virtual tables.  

To carry out the process of SAP to BigQuery data replication, the data has to be either SAP HANA or any other platform that is supported by SAP. The data so replicated can be used to create a backup in SAP which in turn may be used with a range of SAP systems to BigQuery. It also provides deep insights from Machine Learning for data analytics whatever might be the scale of the systems. Replication from SAP to BigQuery can be easily handled by any SAP system admin who has the necessary skillsets in working with SAP DS, SAP Basis, and Google Cloud.

How does SAP to BigQuery work?

Before starting on SAP to BigQuery data replication, it has to be ensured that the database server, SAP Data Services, and the SAP application systems are preinstalled and configured correctly. Also, verify whether all license clauses are complied with for replication. However, there are no hard and fast rules in this regard and the prerequisites for SAP to BigQuery depend entirely on whether data is exported from an ancillary database or an application in SAP.

After these steps are completed, the actual data replication from SAP to BigQuery may be initiated and the workflow will be as follows.

·        Updating data in the source systems by the SAP applications

·        All modifications to the data are replicated by the SAP LT Replication Server. It is then stored in the Operational Delta Queue.

·        A subscriber of the Operational Delta Queue, the SAP DS, tracks the queue at pre-set intervals to verify any changes to the data.

·        Finally, the SAP DS extracts the data from the delta queue that processes and formats it to match the structure supported by BigQuery. Once completed, the process to load the processed data from SAP to BigQuery is started.

A major advantage of replicating data from SAP to BigQuery is that the timing of loading can be set as per convenience. The exported data overwrites any data that is already present in BigQuery. After the replication is done, it is not necessary to continually keep the data in BigQuery (target) in sync with the source systems. Any changes after the main replication are updated by SAP Change Data Capture feature.

[techsciresearch]

Automotive segment to dominate the Saudi Arabia Inductor market till 2026 -TechSci Research

Surge in the application of the passive electronic components and rise in demand for wireless devices is expected to drive the demand for Saudi Arabia Inductor market in the forecast period.

According to TechSci Research report, “Saudi Arabia Inductor Market By Inductance (Fixed, Variable) By Core Type (Air, Ferrite, Iron) By Shield Type (Shielded, Unshielded) By Mounting Technique (Surface Mount, Through Hole) By Application (General Circuits, Power Applications, High-Frequency Applications) By End User (Automotive, Industrial, Telecom, Military & Defense, Healthcare & Others), By Region, Company Forecast & Opportunities, 2026”, The Saudi Arabia Inductor market is expected to grow at a steady rate in the forecast period. Inductors are a kind of passive electrical component which stores the electrical energy in the magnetic field when the current flows through it. The rising demand for consumer electronics such as smartphones, tablets, laptops, portable gaming units, modems, printers, amongst others is driving the demand for inductors in the region. Inductors are deployed in the power circuitry of various devices and the inductor functions as a storage device in the switch-mode supplies that are used in the computers. Smartphones are in high demand in the region owing to the presence of fingerprint verification to control the manufacturing units or smart homes. A large number of inductors are used for the manufacturing of advanced smartphones which is expected to accelerate the inductor market growth in the forecast period. The high penetration of the internet and the growing disposable income is accelerating the demand for inductors in the next five years. The rapid urbanization of people in search of better job opportunities and quality living standards is expected to influence the market growth owing to the rise in demand for advanced products and technologies. Increase in demand from the prominent industry verticals such as aerospace and defense, industrial and healthcare sector the demand for inductors is expected to witness significant growth. Development of the e-commerce channels and the rise in the number of consumers preferring the online channels due to the presence of easy pick-up and delivery facilities and easy online transaction gateways is expected to increase the purchase of home appliances and premium appliances.

The COVID-19 outbreak across the world which has been declared as a pandemic by World Health Organization has affected several countries adversely. Leading authorities of Saudi Arabia imposed lockdown restrictions and released a set of precautionary measures to contain the spread of novel coronavirus. Manufacturing units were temporarily closed down or were not able to work at their full capacity and the workers moved back to their native places which led to the shortage of workforce in the manufacturing industries which negatively impacted the market growth. Disruption in the supply chain was observed which adversely impacted the prominent industries including automotive, consumer electronics, transmission & distribution, amongst others. After the government regains control of the pandemic situation, the market is expected to pick up the pace eventually.

Browse XX market data Tables and XX Figures spread through XX Pages and an in-depth TOC on "Saudi Arabia Inductor Market”.

https://www.techsciresearch.com/report/saudi-arabia-inductor-market/7650.html

Saudi Arabia Inductor market is segmented into inductance, core type, shield type, mounting technique, application, end user, regional distribution, and company. Based on end user, the market can be divided into automotive, industrial, telecom, military & defense, healthcare & others. The automotive industry is expected to hold a major market share in the forecast period, 2022-2026. The rising demand for automation in automobiles to enhance the consumer experience and provide more comfort is increasing the need to integrate electronic devices in automobiles. Due to the increase in the number of electronic components in automobiles the demand for the inductor market is expected to witness considerable growth in the next five years.

Kemlite Piping Solution, TDK-Lambda EMEA, Panasonic Middle East, AVX Corporation, Delta Electronics, Pulse Electronics, Abracon Corporation, Murata Manufacturing, Avnet, Alfanar, among others are some of the leading players operating in Saudi Arabia Inductor market. Companies operating in the market are using strategies such as joint ventures, product launches, mergers, and research collaborations to boost their share and increase their geographic reach.

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Customers can also request for 10% free customization on this report.

“Market players are investing huge amounts for the technological up-gradation of the existing infrastructure and the miniaturization of consumer electronic devices. The rise in demand for sleek and compact electronic devices occupying less space and reducing the thickness of the device is driving the demand for the inductors. Roll-out of the 5G technology and the growing advancements in the telecommunication network in addition to the growing demand for smart grids is expected to contribute significantly to the market growth till 2026” said Mr. Karan Chechi, Research Director with TechSci Research, a research based global management consulting firm.

“Saudi Arabia Inductor Market By Inductance (Fixed, Variable) By Core Type (Air, Ferrite, Iron) By Shield Type (Shielded, Unshielded) By Mounting Technique (Surface Mount, Through Hole) By Application (General Circuits, Power Applications, High-Frequency Applications) By End User (Automotive, Industrial, Telecom, Military & Defense, Healthcare & Others), By Region, Company Forecast & Opportunities, 2026”, has evaluated the future growth potential of Saudi Arabia Inductor market and provides statistics & information on market size, structure and future market growth. The report intends to provide cutting-edge market intelligence and help decision makers take sound investment decisions. Besides, the report also identifies and analyzes the emerging trends along with essential drivers, challenges, and opportunities in Saudi Arabia Inductor market.

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[juniperpublishersoceanography]

Effect of Different Water Sources on Survival Rate (%) Growth Performance, Feed Utilization, Fish Yield, and Economic Evaluation on Nile Tilapia (Oreochromis niloticus) Monosex Reared in Earthen Ponds- Juniper Publishers

Abstract

The aim of the present study was to investigate the effect of water source on survival rate %, growth performance, feed utilization, fish yield, economic evaluation and production of Nile tilapia (Oreochromis niloticus) monosex reared in earthen ponds. Nine earthen ponds were used and divided into three categories of three earthen ponds each. The average size of each pond was approximately 5200m2, 6000 monosex all male Nile tilapia were used in each pond and were stocked for 192 days. The fingerlings average weight was 4.38±0.03g/ fish, the fish were fed using a floating feed 25% crude protein, and were fed at a daily rate of 3% of their body weight. Results showed that body weight was increased significantly (P<0.05) with well water to 472.33g/fish. While were 354.17 and 320.17g/fish for fresh and agricultural drainage water, respectively. Specific growth rates (SGR%) increased with well water compared to both fresh and drainage water. Feed conversion ratio (FCR) and protein efficiency ratio (PER) were improved with Agriculture drainage water. Survival rates with fresh and well water were 98.53% and 98.31% respectively, however, was 95.05% with Agriculture drainage water. Total fish yield were affected significantly by treatments. It was 2128, 1921.8, and 2837.7kg at fresh, drainage and well water respectively. Net return arrived to 12996 for well water source when it was 6784LE for agricultural drainage water and 9158LE for fresh water.

Keywords: Water resources; Nile tilapia; Growth per

Introduction

Nile tilapia, Oreochromis niloticus (Linnaeus) is one of the most important freshwater fish in world aquaculture [1]. It is widely cultured in many tropical and subtropical countries of the world [2]. Rapid growth rates, high tolerance to adverse environmental conditions, efficient feed conversion, ease of spawning, resistance to disease and good consumer acceptance make it a suitable fish for culture [3]. Farmed tilapia production increased semi dramatically in recent years, increasing from 383,654mt in 1990 to 2,326,413mt in 2006 [4]. Tilapia has established a secure position in a number of water impoundments of India. But, its performance in open water ponds of the country has been discouraging over the years [5]. For tilapia aquaculture is excessive reproduction and the resulting small size of the fish produced.

Egypt has suitable natural conditions for desert aquaculture. Egypt has vast resources of groundwater [6]. Fresh groundwater resources in Egypt contribute 20% to the potential water resources in Egypt. One of the groundwater resources is the Nile Valley and Delta system with the storage capacities of 200 billion m3 and 300 billion m3, respectively. Oasis water in the west desert, Bahariya, Farafra, Dakhla, Kharga, and Siwa, were established from underground natural wells and springs.

With the prohibition of the establishment of fish farms on agricultural land, with the prohibition of the use of Nile water for fish farming, with increased competition for spaces adjacent to the lakes and sources of agricultural drainage water, despite its disadvantages, has caused the possession of new fish farm in the Nile [7]. Valley of the most difficult things and out of reach. Hence the search for an alternative to invade the desert, especially with the development of methods of fish farming and providing the requirements of education and with the provision of underground water of the highest purity with different salinity (fresh & brackish & marine) and where the trained professionals are available [8]. In the hope to produce clean fish with improved quality and cheaper than other animal proteins we conducted the present research in a private fish farm located in the desert belonging to Noubaria Agricultural Development company (Ragab Farms) aiming to study the effect of water source on survival rate (%) growth performances, feed conversion ratio, protein efficiency ratio, annul fish yield and profitability Nile tilapia (Oreochromis niloticus) monosex commercial farming.

Materials and Methods

Water Source

Three types of water sources: fresh water, agricultural drainage water, and well water were compared in the present experiment. Water supplies were replaced three times during the experimental period (192 days).

Experimental design

Nine earthen ponds (5200m2) were used in these experiment were divided into three categories of earthen ponds even three ponds represent one treatment (fresh water, drainage water and well water.

Stocking density

6000 monosex all male Nile tilapia (Oreochromis niloticus) fingerlings of average weight (4.38±0.03g/ fish) were stoked in each pond on April 11, 2007 and observed through October 19, 2007. The area of each pond 5200m2.

Experimental Fish

Fingerlings of all male Nile tilapia (Oreochromis niloticus) monosex were collected from Noubaria Agricultural Development Company (Ragab Fish Hatchery) and were over wintered in earthen ponds to provide suitable fingerlings for the beginning of the growing season. All ponds in this experiment were sampled monthly using a cast net method. Sample sizes were 1% of the stocked numbers and the average individual fish weight was calculated to determine growth rates. Then, with these calculations, the feed amounts were adjusted for the following month.

Experimental diet

The floating commercial diet used in this experiment was fed at a daily rate of 3% of the fish body weight by using self feeders The ingredients of the commercial diet used in the experiment is presented in (Table 1). The dietary composition of vitamin and mineral premix is listed in. Fish were fed a floating ration for 6 days per week. Feeding rate was adjusted monthly based upon the calculated biomass of fish obtained through the monthly sampling and assumption of 100% survival.

Water quality

Physical parameters: Water temperature °C was determined at every days in the experiment.

Chemical Parameters: Samples for determination of dissolved oxygen (DO) were immediately fixed after sampling and DO concentration was determined according to Winkler's technique. Methods described by Golterman et al. [9] were used in determination of ammonia. Also pH was measured by digital pH meter (Orion model 720 A, s /No 13602) in all experiments.

Chemical Analysis of the commercial of Diet: Chemical analysis of the commercial diet used in the experiment was done according to AOAC (2000) as shown in Table 1.

Growth parameters and Statistical analysis: Data on growth, feed utilization, survival rate and proximate and chemical composition of whole fish body were subjected to one-way ANOVA [10]. To locate significant differences between fish size within different water resources of pond. Duncan's multiple rang test [11] was done. All percentages and ratio were transformed to arcsine values prior to analysis [12].

Results and Discussion

Experimental diet

The commercial diet used in the present experiment contained 25% CP and 4.3kcal/g gross energy (Table 1). Although there are large variations in the data available about the optimum protein level for tilapias which range between 20 and 40% crude protein [13-15] practical diets as low as 25% protein was successfully used for rearing monosex tilapia [3].

Vit. A 8000 I.U. Vit. D3 4000 I.U.; vit. E 50mg; Vit. k3 19mg;

Vit. B1 40mg; vit. B2 25 mg; Vit. B6 125mg; vit B12 69mg;

Pantothenic acid 40mg; Nicotinic acid 125mg; Folic acid 400mg;

Water quality

Collected data on water temperature and dissolved oxygen (DO), pH and ammonia are summarized in Tables 2-4. Water temperature throughout the present experiments ranged between 24.13±0.53 and 30.26±0.45 °C in fresh water experiment, 24.23±0.53 and 30.65±0.53 °C in drainage water experiment and between 29.94±0.12 and 33.63±0.43 in well water experiment which was the high temperature and closely related to the average of optimal value for tilapia (28-30 °C). Our results were agreement with Broussard [16] reported that tilapia as a warm water fish that dominate African lakes, are known to grow well in high temperature. The fluctuation of water temperature are reached its maximum values during August, however its minimum were during April and November.

*Each value was on average of four sub samples

Biotin 20mg; cholin chloride 80 mg; copper 400mg; Iodine 40mg;

Iron 120mg; Manganese 220mg; Zink 22mg; Selenium 4mg

Means in the same column having different letters are significantly different (P<0.05).

Overall means of water dissolved oxygen (DO) throughout the present experiment were 7.20±0.37mg DO/I for fresh water, 7.19±0.36mg DO/I for drainage water and 6.33±0.36mg DO/I for well water. The fluctuation of water dissolved oxygen (DO) showed that the maximum values of DO were obtained in November for the fresh and drainage water and August in well water, however, the lowest values were in April. In general, dissolved oxygen levels were within the high standards and higher than cited by Boyd [18] for good production of tilapia (4.20 to 5.90mg DO/I) in aquaculture ponds. One of the most important environmental factors is dissolved oxygen. It is considered a limiting factor for success or failure in intensive culture. An excellent aquaculture attribute of tilapia is their tolerance to low dissolved oxygen concentration [16]. The dissolved oxygen content in earthen ponds depends on the pond water temperature, fish biomass and rate of water exchange [18]. Chervinski [19] reported that O. niloticus survived short term exposure to 0.1mg DO/ l. However, Collins [20] observed in a review on oxygen concentration of various studies, that growth rate of non-salmonid fish was increasingly depressed as dissolved oxygen fall below 50% saturation. Rappaport et al. [21] reported that growth of carp was reduced by predawn dissolved oxygen less that 25% saturation. Tichert-C & Green [22] compared the growth of tilapia monosex in earthen ponds aerated or unaerated at 10 or 30% saturation of dissolved oxygen. They found that tilapia production and final weight were significantly greater in aerated ponds than unaerated ponds.

The water pH values throughout the present experiments ranged between 8.00±0.13and 8.10±0.13 with an overall mean of 8.04±0.13 in fresh water and ranged between 8.01±0.13 and 8.10±0.13 with an overall mean of 8.05±0.13 in drainage water and ranged between 7.98±0.13and 8.01±0.13 with an overall mean of 8.00±0.13 in well water. The fluctuations of pH reach the highest value of 8.10+0.13 during August in fresh and drainage water and 8.01+0.13 in well water. The results showed that the present pH values are suitable. For rearing tilapia monosex in earthen ponds. Johnson [23] recommended the range of pH 6.5 to 9.0 for most of freshwater fish species.

The water un-ionized ammonia (NH3) throughout the present experiments ranged between 0.09±0.01 and 0.12±0.01 with an overall mean of 0.11±0.01 in fresh water and ranged between 0.10±0.01 and 0.13±0.01 with an overall mean of 0.11±0.01 in drainage water and ranged between 0.06±0.01 and 0.10±0.01 with an overall mean of 0.077±0.01 in well water. The fluctuations of un-ionized ammonia reach the highest values of 0.13mg/ l during August. Unionized ammonia concentrations in the experimental ponds generally remained below levels which would cause chronic toxicity problems in tilapia. Tilapia is more tolerant to elevated levels of ammonia than more other sensitive species such as salmonids [23]. Some tilapias have been shown to acclimate to higher levels of ammonia after chronic exposure to low levels [24]. Johnson [23] showed that levels of un-ionized ammonia which may adversely affect growth in tilapia range from 1mg/ l to 2mg/ l ammonia where temperature and pH are within normal range.

Growth performance of tilapia monosex

Mean weight: Results of the present study showed that the mean weights at all rearing intervals different significantly (P<0.05) during all the experimental periods (Table 5 & Figure 1). Averages of fish body weights for fresh water, drainage water and well water were found to be 23.16, 18.66 and 25.16g, respectively after the 1st month of stocking. The statistical evaluation of results indicated that live weights at this period increased significantly (P<0.05) with using well water. A similar trend was also observed in fish body weights during the other growing periods. At harvest average body weight of fish stocked at well water was significantly (P<0.05) higher than that of fish stocked in fresh or drainage water, which indicates that weights fish were decreased in fresh and drainage water with increasing for used well water at harvest were 354.17g, 320.17g and 472.33g for fresh, drainage and well water, respectively. This significant advancement in fish body weights with increasing at higher temperature of water advocated by Azaze et al. [25] reported that the final mean weight was significantly higher at 26 and 30 °C than at 22 and 34 °C. This finding agrees with our results.

Average daily gain (ADG g/day): Results presented in Table 5 revealed that water sources, affected significantly (P<0.05) ADG during all experimental periods tested (30, 60, 90.120.150.180 and 210 days after start). In general these results indicated that the well water favored significantly ADG of the tilapia monosex in intensive culture system. The results of this point were in agreement with those found by [17] who grew O. niloticus from 49g to 271g in 122 days (1.4%/day). Siddiqui et al. [15] found that ADG of tilapia O. niloticus reared for 98 days at different water exchange in outdoor concrete tanks was 1.06g / day at 30% dietary crude protein. In the present study the average daily gain was higher with 25% crude protein at all treatments. However, the optimal feeding rate depends on fish size and Specific Growth Rate (SGR %): Results presented in Table 5, revealed that water sources, affected significantly (P<0.05) SGR% during all experimental periods tested (30, 60, 90.120.150.180 and 205 days after start). In general these results indicated that the well water, favored significantly (P<0.05) SGR% of the tilapia monosex in intensive culture system.

During all tested experimental periods tested (30, 60, 90.120.150.180 and 205 days after start) SGR% increased significantly (P<0.05) in almost linear manner in the well water than fresh and drainage water In the present study SGR% values in case of well water continuously higher than fresh or drainage water in all experimental periods. This may be due to the higher temperature of the well water (average 31.94 °C) compared to 27.47 and 27.81 °C for fresh and drainage water, respectively. The results obtained in SGR% are in agreement with those found by Eid & El Denasoury [27] who indicated that increasing temperature from 16 °C to 27 °C improve growth rate of Nile tilapia, which using well water.

Feed conversion ratio (FCR): Results presented in Table 5, show that there were significant (P<0.05) effects of water sources on FCR, feed conversion ratio was observed at harvest was 2.87 at fresh water, followed by 2.83 at well water and 2.80 at drainage water and 2.94 for 1700m2 followed by 2.89 for 4000m2 followed by 2.75 for 5200m2 and was 2.57 for 6000 fish/ acre, 2.75 for 8000 fish/ acre and 2.78 for 10000 fish/ acre. The analyses of variance of the FCR values are presented in Table 5. The FCR is affected by the physiological state of the fish, environmental condition, [28]. Lovshin et al. [29] found that FCR for all male tilapia in earthen ponds was higher (4.3) than when compared with all male and female tilapia in earthen ponds (FCR=7.2). while, fish growth is affected by the amount of feed consumed and the efficiency of assimilation [30].

Protein efficiency ratio (PER): Results of protein efficiency ratio (PER) are presented in Table 5, There were significant (P<0.05) effects of water sources, on PER, it improved significantly (P<0.05) with each increase in pond sizes and decrease stocking density throughout the experimental periods. The best PER observed at harvest was 1.42 with drainage water, followed by 1.40 at well water and 1.38 at fresh water Nyina-W et al. [31] confirmed that when protein supply is appropriate (400500g protein/kg feed for percid fish), different lipid contents in feeds do not have an impact on the rearing results of pikeperch.

Fish survival rate: Results in Table 6 showed that survival rates were changed significantly (P<0.05) by water resources, in fresh and well water were insignificantly (P<0.05) different but survival of the fish in drainage water was 95.05% which was less than survival rates in both fresh water and well water indicating the probable effect of some faction of water quality.

Fish yield: Results of Table 4 show fish yield (kg) per acre as affected by water sources,. Results revealed that total yield increased significantly (P<0.05) with well water. The total production was found to be 133.34% and 90.31% for well water and drainage water, respectively, while it was found to be 76.33% and 68.84% for 4000m2 and 5200m2.

The results of the present experiment were similar to those of Tal & Ziv [32] who showed that the net yield of tilapia monosex in earthen ponds was 16750Kg /ha (7035.0kg/ acre) after 100 days of stocking of 80.000 fish/ha, (33600 fish/acre, 8 fish/m2) on the other hand Eid & Denasoury [27] indicated that increasing temperature from 16 °C to 27 °C improved growth rate of Nile tilapia. Watanabe et al. [33] found that growth rates generally increase with increasing temperature and where markedly lower at 22 °C and well water is the best because the temperature constant through the year and the best quality of the water. [34] found the higher yield obtained in small pond sizes because the bigger ponds with greater surface area were more difficult to manage and often resulted in lower fish yields.

All fish species are characterized by an ideal range of temperature in which they show their maximum growth [3537]. Several studies have been reported that the specific water temperature range showed the faster growth in Pikeperch, Sander lucioperca at 20 °C to 25 °C [38-40]. Low temperature causes sluggishness by retarding the digestion speeding of fish [41]. Some researchers have found that the digestion rate has been increased as the temperature increases [42]. Environmental temperature is one of the most important ecological factor which also influence the behavior and physiological process of aquatic animals [43].

One of the major advantages of groundwater sources is their constant temperature throughout the year. Shallow sources of groundwater approximate the mean air temperature of the area. The chemistry of groundwater is directly dependent on the geology of the area surrounding the source. In limestone areas, groundwater is hard, and high in calcium and carbon dioxide [44]. In areas of granite formation, the groundwater tends to be soft, low in dissolved minerals and carbon dioxide. As will be discussed later, there are advantages and disadvantages to both, emphasizing the need for early extensive water quality testing.

Water temperature has substantial effect on fish metabolism. In response to decreasing of water temperature the enzyme activity of tissues have been increased [45]. Velmurugan et al. [46] have investigated that histopathological and tissue enzyme changes of C. gariepinus exposed to nitrite when water temperatures changes from 27 °C to 35 °C. In a stressful and unfavorable environmental condition GPT and GOT may increase in blood serum. In the present study serum GPT and GOT level were affected by different water temperature. Serum GPT and GOT amount in different fish fed at 20 °C are comparatively lower than those of fish fed at 16 °C and 24 °C experiments (Tables 1-3). These results indicated that 20 °C may be a favorable water temperature for better growth of 16g juvenile Korean rockfish [47,48].

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SEASIDE SECLUSION TRAVEL WITH CONFIDENCE TO LOS CABOS, MEXICO

SEASIDE SECLUSION

TRAVEL WITH CONFIDENCE TO LOS CABOS, MEXICO

…And just like that we’ve moved into a bright new year. I hope your spirits are high and you are filled with optimism for the joys 2021 can bring. Although circumstances from last year have not entirely been erased, I hope the turn of the year for you feels like a fresh, inspiring beginning.

I also hope your aspirations include travel moments (big or small) to look forward to. Now more than ever, I am committed to helping you navigate travel complexities and provide insights into destinations you can visit with peace of mind. If you are considering a winter or spring escape, one such destination is Los Cabos.

The mere mention of Mexico brings to mind ease and secluded serenity — and while you may not currently associate these words with travel, I've designed a comprehensive, yet flexible, travel package including flights, transfers, gorgeous accommodations and the finest on-site experiences at Montage Los Cabos, and even insurance. Book by February 15, and travel by April 30, 2021 to receive exclusive added value — including a complimentary 5th night!

Enjoy learning more below, and don't miss this 1-minute video showcasing what your experience at Montage Los Cabos could look like with safety protocols in place. I wish you and your loved ones good health and happiness, and am looking forward to working with you this year to help you travel with confidence.

TRAVEL WITH CONFIDENCE

YOUR SAFETY & COMFORT IS TOP-OF-MIND

Savvy travelers who are selective about their destinations and travel choices can travel to Mexico with ease and peace of mind. In short, research is key — and I've got you covered. Currently, there are no government restrictions to and from Mexico. You will simply need to fill out a health form before boarding your flight, which can be done from your phone. Masks must be worn during your flights and private transfers — and just like those around you, practice social distancing and sanitization out of respect for your own health and others.

Air travel to Los Cabos International Airport is served by all three U.S. legacy carriers (American, Delta, and United) from most major cities. Each airline has adopted new and thorough aircraft cleaning procedures to provide added confidence that your flight will be as safe and comfortable as possible. From the airport, you are just a 40-minute chauffeured ride away from Santa Maria Bay, home to the five-star Montage Los Cabos.

STAY WITH CONFIDENCE

WELCOME TO MONTAGE LOS CABOS

More than ever, where you stay is just as important as where you go. At Montage Los Cabos, enhanced protocols and safety measures are in accordance with CDC guidelines, ensuring peace of mind throughout your stay. During check-in, all touch points are sanitized with UV wands and luggage delivery is contactless. Hand sanitizers and wipes are provided for guests in addition to your VIP welcome amenity.

All rooms, suites, and casas feature ocean views and are flooded with fresh open air (complete with outdoor showers). Accommodations are disinfected by hotel staff who are health-certified daily and wear gloves and masks before entering rooms. As you take it all in, be prepared to snap quite a few impromptu shots around the resort — the minimalist architecture and lush native landscaping are an ideal canvas for photogenic moments.

Private perks:

✅Complimentary 5th night, ✅Upgrades, early check-in/late check-out ✅Daily breakfast, $200 dining credit ✅VIP welcome amenity ✅Wi-Fi, and more. Book this exclusive package by February 15, and travel by April 30, 2021.

DINE WITH CONFIDENCE

GOURMET DINING WITH ICONIC MEXICAN HOSPITALITY

With several mouthwatering restaurants, strict food service protocols, and $200 of dining credit (my compliments!), be prepared to wine and dine safely across Montage Los Cabos. Head to Marea by Day for familiar Baja cuisine (think fish tacos and cold cerveza), or head back in the evening for more elegant plates. Talay, which means “sea” in Thai, is a unique night-market setting offering Thai street food from a gourmet food truck. For modern, gourmet Mexican with inspired gastronomic twists, be sure to spend an evening at Mezcal.

At all venues, guests are seated six feet apart at reduced capacity, hand sanitizers are on every table, and storage bags are provided for your used masks and napkins for the added protection of both guests and staff. If you prefer your free daily breakfast in-room, a QR code menu allows for contactless room service, where your colorful breakfast is left just outside your door for minimal interaction.

EXPERIENCE WITH CONFIDENCE

STUNNING BEACH, POOL, GOLF & SPA

While Cabo offers plenty of offsite excursions for adventure seekers — from ATV tours to deep sea fishing — there is plenty to do without straying far from the comfort and safety of the resort. Cozy up in an isolated beach bed or pool cabana to gaze at the yachts dotting the sparkling shoreline. Enjoy a complimentary kayak, paddle board, or snorkel set to discover the playful Pacific sea life up close. Round out your adventures at sea with a socially distant cooking class or tequila tasting.

After a long year of not-so-ideal habits (guilty), a soothing wellness experience awaits at the most spacious spa on the Baja Peninsula, complete with an Olympic size adults-only pool. All spa facilities are free to enjoy (a major value of the resort) with a pre-entry temperature check for both guests and technicians. If you haven’t had the pleasure of golfing lately, we recommend the Swing Your Swing Massage to improve your balance and rotation before heading out to the signature course — all 19 holes feature a view of the Sea of Cortez.