#big data clusters in sql server
Explore tagged Tumblr posts
Text
Unlocking Data Potential with SQL Server 2019 Standard
Transforming Data Management: Embrace the Next Generation of SQL Server
In today’s rapidly evolving digital landscape, data has become the cornerstone of successful business strategies. The future of your data lies in harnessing powerful and reliable tools that can handle complex workloads, ensure security, and deliver actionable insights. SQL Server Standard 2019 stands out as a formidable choice, offering a modern data platform designed to empower organizations of all sizes.
SQL Server 2019 introduces advanced features that redefine how businesses manage, analyze, and secure their data. Its ability to handle big data clusters seamlessly integrates traditional relational data with big data analytics, enabling organizations to gain comprehensive insights from diverse data sources. This integration facilitates more informed decision-making and drives innovation across various sectors.
One of the most significant enhancements is the intelligent database performance. SQL Server 2019 leverages artificial intelligence and machine learning to optimize queries and automate routine tasks. This not only improves efficiency but also reduces operational costs, allowing IT teams to focus on strategic initiatives. The platform’s adaptive query processing dynamically adjusts execution plans, ensuring optimal performance even with growing data volumes.
Security remains a top priority in SQL Server 2019. With enhanced security features such as data encryption, threat detection, and vulnerability assessments, organizations can safeguard sensitive information against cyber threats. These capabilities are crucial in an era where data breaches can have devastating consequences for reputation and compliance.
The modern data platform also supports hybrid cloud environments, offering flexibility and scalability. Organizations can deploy SQL Server 2019 on-premises, in the cloud, or in a hybrid setup, tailoring their infrastructure to meet specific needs. This adaptability ensures that your data strategy remains agile and future-proof.
For businesses looking to adopt or upgrade their SQL Server environment, understanding licensing costs is essential. You can explore the sql server 2019 standard license cost, which provides transparent pricing options tailored to various organizational sizes and requirements.
As we look ahead, the future of data management is bright with SQL Server 2019 leading the way. Its robust features, combined with a focus on security and performance, make it an ideal choice for organizations aiming to harness the full potential of their data. By investing in this technology today, you set the stage for innovation, efficiency, and competitive advantage tomorrow.
Discover how SQL Server Standard 2019 can transform your data strategy and unlock new opportunities. Stay ahead in the data-driven world and empower your business to thrive in the digital age.
#buy sql server 2019 standard key#big data clusters in sql server#intelligent database performance#enhanced security features#modern data platform for business
0 notes
Text
KNIME Software: Empowering Data Science with Visual Workflows
By Dr. Chinmoy Pal
In the fast-growing field of data science and machine learning, professionals and researchers often face challenges in coding, integrating tools, and automating complex workflows. KNIME (Konstanz Information Miner) provides an elegant solution to these challenges through an open-source, visual workflow-based platform for data analytics, reporting, and machine learning.
KNIME empowers users to design powerful data science pipelines without writing a single line of code, making it an excellent choice for both non-programmers and advanced data scientists.
🔍 What is KNIME?
KNIME is a free, open-source software for data integration, processing, analysis, and machine learning, developed by the University of Konstanz in Germany. Since its release in 2004, it has evolved into a globally trusted platform used by industries, researchers, and educators alike.
Its visual interface allows users to build modular data workflows by dragging and dropping nodes (each representing a specific function) into a workspace—eliminating the need for deep programming skills while still supporting complex analysis.
🧠 Key Features of KNIME
✅ 1. Visual Workflow Interface
Workflows are built using drag-and-drop nodes.
Each node performs a task like reading data, cleaning, filtering, modeling, or visualizing.
✅ 2. Data Integration
Seamlessly integrates data from Excel, CSV, databases (MySQL, PostgreSQL, SQL Server), JSON, XML, Apache Hadoop, and cloud storage.
Supports ETL (Extract, Transform, Load) operations at scale.
✅ 3. Machine Learning & AI
Built-in algorithms for classification, regression, clustering (e.g., decision trees, random forest, SVM, k-means).
Integrates with scikit-learn, TensorFlow, Keras, and H2O.ai.
AutoML workflows available via extensions.
✅ 4. Text Mining & NLP
Supports text preprocessing, tokenization, stemming, topic modeling, and sentiment analysis.
Ideal for social media, survey, or academic text data.
✅ 5. Visualization
Interactive dashboards with bar plots, scatter plots, line graphs, pie charts, and heatmaps.
Advanced charts via integration with Python, R, Plotly, or JavaScript.
✅ 6. Big Data & Cloud Support
Integrates with Apache Spark, Hadoop, AWS, Google Cloud, and Azure.
Can scale to large enterprise-level data processing.
✅ 7. Scripting Support
Custom nodes can be built using Python, R, Java, or SQL.
Flexible for hybrid workflows (visual + code).
📚 Applications of KNIME
📊 Business Analytics
Customer segmentation, fraud detection, sales forecasting.
🧬 Bioinformatics and Healthcare
Omics data analysis, patient risk modeling, epidemiological dashboards.
🧠 Academic Research
Survey data preprocessing, text analysis, experimental data mining.
🧪 Marketing and Social Media
Campaign effectiveness, social media sentiment analysis, churn prediction.
🧰 IoT and Sensor Data
Real-time streaming analysis from smart devices and embedded systems.
🛠️ Getting Started with KNIME
Download: Visit: https://www.knime.com/downloads Choose your OS (Windows, Mac, Linux) and install KNIME Analytics Platform.
Explore Example Workflows: Open KNIME and browse sample workflows in the KNIME Hub.
Build Your First Workflow:
Import dataset (Excel/CSV/SQL)
Clean and transform data
Apply machine learning or visualization nodes
Export or report results
Enhance with Extensions: Add capabilities for big data, deep learning, text mining, chemistry, and bioinformatics.
💼 KNIME in Enterprise and Industry
Used by companies like Siemens, Novartis, Johnson & Johnson, Airbus, and KPMG.
Deployed for R&D analytics, manufacturing optimization, supply chain forecasting, and risk modeling.
Supports automation and scheduling for enterprise-grade analytics workflows.
📊 Use Case Example: Customer Churn Prediction
Workflow Steps in KNIME:
Load customer data (CSV or SQL)
Clean missing values
Feature engineering (recency, frequency, engagement)
Apply classification model (Random Forest)
Evaluate with cross-validation
Visualize ROC and confusion matrix
Export list of high-risk customers
This entire process can be done without any coding—using only the drag-and-drop interface.
✅ Conclusion
KNIME is a robust, scalable, and user-friendly platform that bridges the gap between complex analytics and practical use. It democratizes access to data science by allowing researchers, analysts, and domain experts to build powerful models without needing extensive programming skills. Whether you are exploring data science, automating reports, or deploying enterprise-level AI workflows, KNIME is a top-tier solution in your toolkit.
Author: Dr. Chinmoy Pal Website: www.drchinmoypal.com Published: July 2025
0 notes
Text
Beginners Must Read: 10 Elements to Master Quantitative Trading of US Stocks
✅ Beginners Must Read: 10 Elements to Master Quantitative Trading of US Stocks Quantitative trading of US stocks is a modern investment method that uses data and models at its core and computer programs to make securities trading decisions. It emphasizes automation, systemization, and strategy-driven, and is widely used in high-frequency trading (HFT) and medium- and low-frequency arbitrage trading strategies.
To fully enter the market and make long-term profits, you need to prepare the following 10 core elements.
I. Technical Conditions
1️⃣ Programming Skills Quantitative trading requires very high coding skills.
Programming Language Applicable Scenario Python Strategy research, backtesting, data processing (ecosystem: Pandas, NumPy, TA-Lib, Backtrader, Zipline) R Statistical modeling and visualization (best for quantitative research) C++ High-frequency trading scenarios, low latency execution Java / C# Building stable trading services SQL queries on structured market and trading data
2️⃣ Data processing capabilities Cleaning: outlier removal, missing value handling Feature engineering: indicator calculation, principal component analysis, normalization, etc. Storage optimization: HDF5, Parquet for efficient storage Parallel computing: Utilizing tools such as Dask, Numba, Ray for big data processing
3️⃣ Computing infrastructure High frequency: Co-location of deployment on local servers or exchanges Medium/low frequency: Wait until sufficient AWS EC2, Google Colab, Azure cloud resources are available Tools: Docker, Redis, GPU acceleration, distributed computing frameworks, etc.
II. Data acquisition
1️⃣ Data types Types Description Historical market data: K-lines, ticks, trade-by-trade Real-time data: Level 1 (snapshot), Level 2 (market) Alternative data: Social media, credit card usage, satellite imagery, etc.
2️⃣ Data providers Name Function AllTick: Tick-level, low latency, global market support Bloomberg/Refinitiv: Commercial-grade data standard Polygon.io/IEX Cloud API: Flexible, suitable for developers Alpha Vantage/Yahoo Finance: Free, suitable for prototype testing of strategies
III. Strategy formulation
1️⃣ Theoretical foundations Financial models: CAPM, APT, Black-Scholes Mathematical tools: Regression analysis, cointegration, Monte Carlo simulation, PCA Machine learning: Classification, clustering, regression, reinforcement learning, XGBoost, LSTM
2️⃣ Types of strategies and backtesting platforms Types of strategies Examples Trend strategies: moving averages, MACD Mean reversion: pairs trading, Bollinger bands Market neutral: statistical arbitrage, quant hedging Event driven: earnings season, M&A, ex-dividend High frequency arbitrage: quote stuffing, latency arbitrage ✅ Recommended backtesting tools: Backtrader, Zipline, QuantConnect, QuantRocket
IV. Trade execution system 1️⃣ API broker access Broker features Interactive Brokers (IBKR) Professional level, FIX API support, global asset coverage Alpaca Zero commission, developer friendly, REST API support TD Ameritrade / Robinhood For retail investors, limited API features 2️⃣ Execution system optimization Uses FIX protocol or ZeroMQ for low latency trading Smart order splitting and exchange routing (smart order routing) Slippage control and detailed analysis of order books
V. Risk Management 1️⃣ Risk Metrics and Indicators Use of Indicators VaR/CVaR Estimated Maximum Loss Sharpe Ratio Balance between Profit and Volatility Maximum Drawdown/Sortino Describes the Extreme Losses of the Strategy 2️⃣ Fund Management Kelly Rule, Fixed Ratio, Dynamic Position Adjustment Stop Loss/Stop Profit Mechanism, Trading Frequency Limit, Drawdown Protection Mechanism
VI. Compliance 1️⃣ Registration and Licensing Register with the SEC (US Securities and Exchange Commission) as an RIA (Investment Adviser) or CTA (Commodity Trading Adviser) High Frequency Traders must register with FINRA/NFA/CFTC to ensure compliance 2️⃣ Regulatory Compliance Comply with Securities and Investment Advisers Act Prohibited Activities: Market Manipulation, Insider Trading, Fraudulent Issuance
VII. Capital Requirements 1️⃣ Initial Capital Investment Type Reference Amount Low Frequency Test Strategy $10,000-$100,000 High Frequency Trading Starting at $500,000 (including servers and data) 2️⃣ Cost structure Fees, data subscription fees, infrastructure
Legal advisors, compliance advisors, human resources
Evaluate the breakeven point of the strategy and determine sustainability
VIII. Team and resources
1️⃣ Team composition Financial engineers: models and backtesting
Data scientists: factor engineering, machine learning research
Programmers: development of execution systems and data pipelines
Risk managers: building indicators, monitoring models
Operation and maintenance engineers: ensuring system stability 24/7
2️⃣ Business mechanisms Deployment of continuous integration strategy with Git + CI/CD
Strategy logging + post-mortem debugging
Quarterly/monthly strategy review mechanism
IX. Market access and permission management
1️⃣ Access methods Access to major exchanges such as NYSE, NASDAQ, CBOE
High frequency strategy recommended Colocation (same computer room as exchange servers)
2️⃣ Permissions and contract management
Naked short selling, options trading, leverage allowed
Check broker API Restrictions on calls, margin trading, etc.
X. Mentality and practical experience
1️⃣ Understanding of market structure
Deep understanding of order book, trade matching, and trade priorities
Responding to exchange delays and changes in trading rules
2️⃣ Self-discipline and stress tolerance
Accept short-term failures of the strategy and avoid subjective interventions
Maintain consistency in the strategy and a long-term perspective
Establish a positive review and knowledge repetition system
✅ Summary: Systematic + Data-driven is the essence of quantitative trading
Quantitative trading of US stocks is not just about "creating a strategy", it is also about technology, finance, engineering, psychology, and law.
Mastering the above 10 preparation elements will significantly improve your ability to:
Strategy win rate and robustness
System stability and execution efficiency
Long-term profitability and risk management ability
0 notes
Text
Hadoop Meets NoSQL: How HBase Enables High-Speed Big Data Processing
In today's data-driven world, businesses and organisations are inundated with huge amounts of information that must be processed and analysed quickly to make informed decisions. Traditional relational databases often struggle to handle this scale and speed. That’s where modern data architectures like Hadoop and NoSQL databases come into play. Among the powerful tools within this ecosystem, HBase stands out for enabling high-speed big data processing. This blog explores how Hadoop and HBase work together to handle large-scale data efficiently and why this integration is essential in the modern data landscape.
Understanding Hadoop and the Rise of Big Data
Hadoop is a framework that is publicly available, developed by the Apache Software Foundation. It allows for the distributed storage and processing of huge datasets across clusters of computers using simple programming models. What makes Hadoop unique is its ability to scale from a single server to thousands of them, each offering local storage and computation.
As more industries—finance, healthcare, e-commerce, and education—generate massive volumes of data, the limitations of traditional databases become evident. The rigid structure and limited scalability of relational databases are often incompatible with the dynamic and unstructured nature of big data. This need for flexibility and performance led to the rise of NoSQL databases.
What is NoSQL and Why HBase Matters
NoSQL stands for "Not Only SQL," referring to a range of database technologies that can handle non-relational, semi-structured, or unstructured data. These databases offer high performance, scalability, and flexibility, making them ideal for big data applications.
HBase, modelled after Google's Bigtable, is a column-oriented NoSQL database that runs on top of Hadoop's Hadoop Distributed File System (HDFS). It is designed to provide quick read/write access to large volumes of sparse data. Unlike traditional databases, HBase supports real-time data access while still benefiting from Hadoop’s batch processing capabilities.
How HBase Enables High-Speed Big Data Processing
HBase’s architecture is designed for performance. Here’s how it enables high-speed big data processing:
Real-Time Read/Write Operations: Unlike Hadoop’s MapReduce, which is primarily batch-oriented, HBase allows real-time access to data. This is crucial for applications where speed is essential, like fraud detection or recommendation engines.
Horizontal Scalability: HBase scales easily by adding more nodes to the cluster, enabling it to handle petabytes of data without performance bottlenecks.
Automatic Sharding: It automatically distributes data across different nodes (regions) in the cluster, ensuring balanced load and faster access.
Integration with Hadoop Ecosystem: HBase integrates seamlessly with other tools like Apache Hive, Pig, and Spark, providing powerful analytics capabilities on top of real-time data storage.
Fault Tolerance: Thanks to HDFS, HBase benefits from robust fault tolerance, ensuring data is not lost even if individual nodes fail.
Real-World Applications of Hadoop and HBase
Organisations across various sectors are leveraging Hadoop and HBase for impactful use cases:
Telecommunications: Managing call detail records and customer data in real-time for billing and service improvements.
Social Media: Storing and retrieving user interactions at a massive scale to generate insights and targeted content.
Healthcare: Analysing patient records and sensor data to offer timely and personalised care.
E-commerce: Powering recommendation engines and customer profiling for better user experiences.
For those interested in diving deeper into these technologies, a data science course in Pune can offer hands-on experience with Hadoop and NoSQL databases like HBase. Courses often cover practical applications, enabling learners to tackle real-world data problems effectively.
HBase vs. Traditional Databases
While traditional databases like MySQL and Oracle are still widely used, they are not always suitable for big data scenarios. Here’s how HBase compares:
Schema Flexibility: HBase does not necessitate a rigid schema, which facilitates adaptation to evolving data needs.
Speed: HBase is optimised for high-throughput and low-latency access, which is crucial for modern data-intensive applications.
Data Volume: It can efficiently store and retrieve billions of rows and millions of columns, far beyond the capacity of most traditional databases.
These capabilities make HBase a go-to solution for big data projects, especially when integrated within the Hadoop ecosystem.
The Learning Path to Big Data Mastery
As data continues to grow in size and importance, understanding the synergy between Hadoop and HBase is becoming essential for aspiring data professionals. Enrolling in data science training can be a strategic step toward mastering these technologies. These programs are often designed to cover everything from foundational concepts to advanced tools, helping learners build career-ready skills.
Whether you're an IT professional looking to upgrade or a fresh graduate exploring career paths, a structured course can provide the guidance and practical experience needed to succeed in the big data domain.
Conclusion
The integration of Hadoop and HBase represents a powerful solution for processing and managing big data at speed and scale. While Hadoop handles distributed storage and batch processing, HBase adds real-time data access capabilities, making the duo ideal for a range of modern applications. As industries continue to embrace data-driven strategies, professionals equipped with these skills will be in huge demand. Exploring educational paths such as data science course can be your gateway to thriving in this evolving landscape.
By understanding how HBase enhances Hadoop's capabilities, you're better prepared to navigate the complexities of big data—and transform that data into meaningful insights.
Contact Us:
Name: Data Science, Data Analyst and Business Analyst Course in Pune
Address: Spacelance Office Solutions Pvt. Ltd. 204 Sapphire Chambers, First Floor, Baner Road, Baner, Pune, Maharashtra 411045
Phone: 095132 59011
0 notes
Text
Snowflake vs Traditional Databases: What Makes It Different?
Data management has evolved significantly in the last decade, with organizations moving from on-premise, traditional databases to cloud-based solutions. One of the most revolutionary advancements in cloud data warehousing is Snowflake, which has transformed the way businesses store, process, and analyze data. But how does Snowflake compare to traditional databases? What makes it different, and why is it gaining widespread adoption?
In this article, we will explore the key differences between Snowflake and traditional databases, highlighting the advantages of Snowflake and why businesses should consider adopting it. If you are looking to master this cloud data warehousing solution, Snowflake training in Chennai can help you gain practical insights and hands-on experience.
Understanding Traditional Databases
Traditional databases have been the backbone of data storage and management for decades. These databases follow structured approaches, including relational database management systems (RDBMS) like MySQL, Oracle, PostgreSQL, and SQL Server. They typically require on-premise infrastructure and are managed using database management software.
Key Characteristics of Traditional Databases:
On-Premise or Self-Hosted – Traditional databases are often deployed on dedicated servers, requiring physical storage and management.
Structured Data Model – Data is stored in predefined tables and schemas, following strict rules for relationships.
Manual Scaling – Scaling up or out requires purchasing additional hardware or distributing workloads across multiple databases.
Fixed Performance Limits – Performance depends on hardware capacity and resource allocation.
Maintenance-Intensive – Requires database administrators (DBAs) for management, tuning, and security.
While traditional databases have been effective for years, they come with limitations in terms of scalability, flexibility, and cloud integration. This is where Snowflake changes the game.
What is Snowflake?
Snowflake is a cloud-based data warehousing platform that enables businesses to store, manage, and analyze vast amounts of data efficiently. Unlike traditional databases, Snowflake is designed natively for the cloud, providing scalability, cost-efficiency, and high performance without the complexities of traditional database management.
Key Features of Snowflake:
Cloud-Native Architecture – Built for the cloud, Snowflake runs on AWS, Azure, and Google Cloud, ensuring seamless performance across platforms.
Separation of Storage and Compute – Unlike traditional databases, Snowflake separates storage and computing resources, allowing independent scaling.
Automatic Scaling – Snowflake scales up and down dynamically based on workload demands, optimizing costs and performance.
Pay-As-You-Go Pricing – Organizations pay only for the storage and computing resources they use, making it cost-efficient.
Zero Management Overhead – No need for database administration, as Snowflake handles maintenance, security, and updates automatically.
Key Differences Between Snowflake and Traditional Databases
To understand what makes Snowflake different, let’s compare it to traditional databases across various factors:
1. Architecture
Traditional Databases: Follow a monolithic architecture, where compute and storage are tightly coupled. This means that increasing storage requires additional compute power, leading to higher costs.
Snowflake: Uses a multi-cluster, shared data architecture, where compute and storage are separate. Organizations can scale them independently, allowing cost and performance optimization.
2. Scalability
Traditional Databases: Require manual intervention to scale. Scaling up involves buying more hardware, and scaling out requires adding more database instances.
Snowflake: Offers instant scalability without any manual effort. It automatically scales based on the workload, making it ideal for big data applications.
3. Performance
Traditional Databases: Performance is limited by fixed hardware capacity. Queries can slow down when large volumes of data are processed.
Snowflake: Uses automatic query optimization and multi-cluster computing, ensuring faster query execution, even for large datasets.
4. Storage & Cost Management
Traditional Databases: Storage and compute are linked, meaning you pay for full capacity even if some resources are unused.
Snowflake: Since storage and compute are separate, businesses only pay for what they use, reducing unnecessary costs.
5. Data Sharing & Collaboration
Traditional Databases: Sharing data requires manual exports, backups, or setting up complex replication processes.
Snowflake: Enables secure, real-time data sharing without data movement, allowing multiple users to access live datasimultaneously.
6. Security & Compliance
Traditional Databases: Require manual security measures such as setting up encryption, backups, and access controls.
Snowflake: Provides built-in security features, including encryption, role-based access, compliance certifications (GDPR, HIPAA, etc.), and automatic backups.
7. Cloud Integration
Traditional Databases: Cloud integration requires additional tools or custom configurations.
Snowflake: Natively supports cloud environments and seamlessly integrates with BI tools like Power BI, Tableau, and Looker.
Why Businesses Are Moving to Snowflake
Companies across industries are transitioning from traditional databases to Snowflake due to its unparalleled advantages in scalability, performance, and cost efficiency. Some key reasons include:
Big Data & AI Adoption: Snowflake’s ability to handle massive datasets makes it ideal for AI-driven analytics and machine learning.
Reduced IT Overhead: Organizations can focus on analytics instead of database maintenance.
Faster Time to Insights: With high-speed querying and real-time data sharing, businesses can make quick, data-driven decisions.
How to Get Started with Snowflake?
If you are looking to switch to Snowflake or build expertise in cloud data warehousing, the best way is through structured training. Snowflake training in Chennai provides in-depth knowledge, practical labs, and real-world use cases to help professionals master Snowflake.
What You Can Learn in Snowflake Training?
Snowflake Architecture & Fundamentals
Data Loading & Processing in Snowflake
Performance Tuning & Optimization
Security, Access Control & Governance
Integration with Cloud Platforms & BI Tools
By enrolling in Snowflake training in Chennai, you gain hands-on experience in cloud data warehousing, making you industry-ready for high-demand job roles.
Conclusion
Snowflake has emerged as a game-changer in the world of data warehousing, offering cloud-native architecture, automated scalability, cost efficiency, and superior performance compared to traditional databases. Businesses looking for a modern data platform should consider Snowflake to enhance their data storage, analytics, and decision-making capabilities.
1 note
·
View note
Text
SQL Server 2019 Standard on Windows 2019: A Powerful Combination for Business Needs
SQL Server 2019 Standard on Windows 2019 is a robust and reliable solution designed to meet the growing demands of modern businesses. This combination of Microsoft’s leading database platform and the latest iteration of Windows Server provides a strong foundation for organizations looking to leverage the power of data management and analytics. SQL Server 2019 offers numerous improvements in performance, security, and flexibility, while Windows Server 2019 enhances the infrastructure with advanced capabilities and modern security features.
One of the key advantages of SQL Server 2019 Standard on Windows 2019 is the overall performance optimization. SQL Server 2019 introduces significant performance improvements over previous versions, such as the intelligent query processing feature, which allows for faster query execution and greater resource efficiency. The ability to handle large volumes of data with increased speed is crucial for businesses that rely on real-time analytics and seamless data access. The SQL server 2019 Standard on Windows 2019 enhanced performance in SQL Server 2019 ensures that organizations can run complex queries and analytics workloads with minimal latency.
SQL Server 2019 Standard also includes an integrated big data cluster feature, which supports the management and analysis of large data sets, such as those from IoT devices or social media platforms. This capability allows businesses to manage structured and unstructured data in a unified platform, offering flexibility and scalability for future growth. Additionally, SQL Server 2019 includes enhanced support for machine learning, allowing users to run R and Python scripts directly within the database. This integration enables organizations to harness the power of artificial intelligence and advanced analytics without the need for external tools or platforms.
Security is another critical aspect of SQL Server 2019 Standard on Windows 2019. With an increasing number of cyber threats targeting businesses, it is essential to protect sensitive data and ensure compliance with industry regulations. SQL Server 2019 enhances security by offering features such as Always Encrypted with secure enclaves, which protect data even while it is being processed. This means that data remains encrypted at all stages, minimizing the risk of unauthorized access or data breaches. Additionally, SQL Server 2019 provides enhanced auditing capabilities and data masking, which help organizations monitor and secure sensitive information.
Windows Server 2019 complements SQL Server 2019 by providing a modern, secure, and scalable environment for hosting databases. With improved support for hybrid cloud environments, Windows Server 2019 makes it easier for businesses to migrate to the cloud or manage hybrid infrastructures. Windows Server 2019 introduces advanced networking features such as Software-Defined Networking (SDN), which improves performance and scalability while simplifying network management. It also includes Windows Defender ATP (Advanced Threat Protection), which offers robust protection against malware and other security threats, ensuring that SQL Server 2019 runs in a secure and protected environment.
In addition to security and performance improvements, SQL Server 2019 Standard on Windows 2019 offers advanced data management features that make it an excellent choice for organizations of all sizes. With built-in data virtualization, businesses can access and query data from multiple sources without having to move or replicate it, reducing the complexity of data management. The platform also supports a wide range of applications, from transactional systems to data warehousing, making it suitable for a diverse set of workloads. Whether businesses are running a small database for a local application or managing an enterprise-level data warehouse, SQL Server 2019 Standard on Windows 2019 provides the scalability and flexibility needed to meet the demands of any use case.
One of the standout features of SQL Server 2019 Standard is its ability to integrate with popular business intelligence (BI) and reporting tools. With support for Microsoft Power BI, businesses can easily create interactive dashboards, reports, and visualizations that help stakeholders make data-driven decisions. SQL Server 2019 also integrates seamlessly with SQL Server Reporting Services (SSRS) and SQL Server Integration Services (SSIS), providing a comprehensive suite of tools for data extraction, transformation, and reporting. This makes it easier for organizations to consolidate their data and gain insights that drive business success.
SQL Server 2019 Standard on Windows 2019 also benefits from enhanced scalability, making it suitable for businesses of all sizes. With the ability to scale up or out depending on the workload, SQL Server 2019 can accommodate both small-scale applications and enterprise-level systems. Whether organizations are managing a single server or a multi-node cluster, SQL Server 2019 provides the tools to manage databases efficiently. The high availability and disaster recovery features in SQL Server 2019, such as Always On Availability Groups, ensure that data is always available, even in the event of hardware failures or other disruptions.
Cost-effectiveness is another advantage of SQL Server 2019 Standard. Compared to the more advanced Enterprise edition, the Standard edition offers many of the same core features at a lower price point, making it an attractive option for businesses with budget constraints. SQL Server 2019 Standard provides a balance between functionality and affordability, making it an ideal choice for small to medium-sized businesses that need a reliable and secure database platform without the high costs associated with enterprise-level solutions.
In conclusion SQL server 2019 Standard on Windows 2019 is a powerful combination that offers businesses a comprehensive solution for database management, analytics, and security. With performance improvements, advanced data management features, and tight integration with modern BI tools, it enables organizations to unlock the full potential of their data. The enhanced security features and scalability ensure that businesses can protect their valuable information while growing and evolving with the demands of the modern enterprise. Whether for small businesses or large organizations, SQL Server 2019 Standard on Windows 2019 is a solution that provides reliability, flexibility, and innovation.
0 notes
Text
7 Best Data Warehouse Tools to Explore in 2025
What is a Data Warehouse?
A data warehouse is a centralized repository designed to store large volumes of data from various sources in an organized, structured format. It facilitates efficient querying, analysis, and reporting of data, serving as a vital component for business intelligence and analytics.
Types of Data Warehouses
Data warehouses can be classified into the following categories:
Enterprise Data Warehouse (EDW): A unified storage hub for all enterprise data.
Operational Data Store (ODS): Stores frequently updated, real-time data.
Online Analytical Processing (OLAP): Designed for complex analytical queries on large datasets.
Data Mart: A focused subset of a data warehouse for specific departments or business units.
Why Use Data Warehouses?
The primary purpose of data warehouses is to store and organize data centrally, enabling faster and more efficient analysis of large datasets. Other benefits include:
Improved Data Quality: Processes ensure data integrity and consistency.
Historical Data Storage: Supports trend analysis and forecasting.
Enhanced Accessibility: Allows seamless access and querying of data from multiple sources.
Who Uses Data Warehouses?
Data warehouses cater to various professionals across industries:
Data Analysts: Query and analyze data for actionable insights.
Data Engineers: Build and maintain the underlying infrastructure.
Business Intelligence Analysts: Generate reports and visualizations for stakeholders.
Analytics Engineers: Optimize data pipelines for efficient loading.
Companies often use data warehouses to store vast amounts of customer data, sales information, and financial records. Modern trends include adopting data lakes and data lake houses for advanced analytics.
Top Data Warehouse Tools to Watch in 2025
1. Snowflake
Snowflake is a cloud-native data warehouse renowned for its flexibility, security, and scalability.
Key Features:
Multi-cluster Architecture: Supports scalability and separates compute from storage.
Virtual Warehouses: On-demand setup for parallel workload handling.
Data Sharing: Facilitates secure data sharing across organizations.
Snowflake integrates seamlessly with tools like dbt, Tableau, and Looker, making it a cornerstone of the modern data stack.
2. Amazon S3
Amazon S3 is a highly scalable, object-based storage service, widely used as a data warehousing solution.
Key Features:
Scalability: Capable of handling any data volume.
AWS Ecosystem Integrations: Enhances processing and analytics workflows.
Cost-effectiveness: Pay-as-you-go pricing model.
Ideal for organizations already leveraging AWS services, Amazon S3 offers unparalleled flexibility and durability.
3. Google Big Query
Google Big Query is a server less, highly scalable solution designed for real-time insights.
Key Features:
Fast Querying: Processes petabytes of data in seconds.
Automatic Scaling: No manual resource management required.
Integrated Machine Learning: Supports advanced analytics.
Big Query’s seamless integration with Google Cloud services and third-party tools makes it a top choice for modern data stacks.
4. Data bricks
Data bricks is a unified analytics platform combining data engineering, science, and business intelligence.
Key Features:
Spark-based Engine: Enables fast, large-scale data processing.
ML flow: Streamlines machine learning lifecycle management.
Real-time Analytics: Processes streaming data effortlessly.
Data bricks supports Python, SQL, R, and Scala, appealing to diverse data professionals.
5. Amazon Redshift
Amazon Redshift is a fully managed, high-performance data warehouse tailored for structured and semi-structured data.
Key Features:
Columnar Storage: Optimized query performance.
Massively Parallel Processing (MPP): Accelerates complex queries.
AWS Integrations: Works well with S3, DynamoDB, and Elastic MapReduce.
Its scalability and cost-effectiveness make it popular among startups and enterprises alike.
6. Oracle Autonomous Data Warehouse
Oracle Autonomous Data Warehouse automates the creation and management of data warehouses using machine learning.
Key Features:
Autonomous Operations: Self-tuning and optimized storage.
Elastic Scalability: Adjusts resources dynamically based on workload.
Built-in ML Algorithms: Facilitates advanced analytics.
Best suited for enterprises seeking robust, automated solutions with high performance.
7. PostgreSQL
PostgreSQL is a versatile, open-source relational database that supports data warehousing needs.
Key Features:
ACID Compliance: Ensures data integrity.
Multi-version Concurrency Control (MVCC): Allows simultaneous access.
Extensibility: Offers plugins like PostgreSQL Data Warehousing by Citus.
Its robust community support and adaptability make PostgreSQL a reliable choice for organizations of all sizes.
Next Steps
Key Takeaways:
Data warehouses enable efficient organization and analysis of large datasets.
Popular tools include Snowflake, Amazon S3, Google BigQuery, Databricks, Amazon Redshift, Oracle, and PostgreSQL.
How to Advance Your Knowledge:
Explore Data Analytics Tools: Get acquainted with platforms like Tableau and dbt.
Learn Data Analytics: Try Career Foundry’s free, 5-day data analytics short course.
Join Live Events: Participate in online events with industry experts.
Take the first step towards becoming a data analyst. Enroll in Career Foundry’s data analytics program and unlock a new career path today.
0 notes
Text
What Is Amazon EBS? Features Of Amazon EBS And Pricing

Amazon Elastic Block Store: High-performance, user-friendly block storage at any size
What is Amazon EBS?
Amazon Elastic Block Store provides high-performance, scalable block storage with Amazon EC2 instances. AWS Elastic Block Store can create and manage several block storage resources:
Amazon EBS volumes: Amazon EC2 instances can use Amazon EBS volumes. A volume associated to an instance can be used to install software and store files like a local hard disk.
Amazon EBS snapshots: Amazon EBS snapshots are long-lasting backups of Amazon EBS volumes. You can snapshot Amazon EBS volumes to backup data. Afterwards, you can always restore new volumes from those snapshots.
Advantages of the Amazon Elastic Block Store
Quickly scale
For your most demanding, high-performance workloads, including mission-critical programs like Microsoft, SAP, and Oracle, scale quickly.
Outstanding performance
With high availability features like replication within Availability Zones (AZs) and io2 Block Express volumes’ 99.999% durability, you can guard against failures.
Optimize cost and storage
Decide which storage option best suits your workload. From economical dollar-per-GB to high performance with the best IOPS and throughput, volumes vary widely.
Safeguard
You may encrypt your block storage resources without having to create, manage, and safeguard your own key management system. Set locks on data backups and limit public access to prevent unwanted access to your data.
Easy data security
Amazon EBS Snapshots, a point-in-time copy that can be used to allow disaster recovery, move data across regions and accounts, and enhance backup compliance, can be used to protect block data storage both on-site and in the cloud. With its integration with Amazon Data Lifecycle Manager, AWS further streamlines snapshot lifecycle management by enabling you to establish policies that automate various processes, such as snapshot creation, deletion, retention, and sharing.
How it functions
A high-performance, scalable, and user-friendly block storage solution, Amazon Elastic Block Store was created for Amazon Elastic Compute Cloud (Amazon EC2).Image credit to AWS
Use cases
Create your cloud-based, I/O-intensive, mission-critical apps
Switch to the cloud for mid-range, on-premises storage area network (SAN) applications. Attach block storage that is both high-performance and high-availability for applications that are essential to the mission.
Utilize relational or NoSQL databases
Install and expand the databases of your choosing, such as Oracle, Microsoft SQL Server, PostgreSQL, MySQL, Cassandra, MongoDB, and SAP HANA.
Appropriately scale your big data analytics engines
Detach and reattach volumes effortlessly, and scale clusters for big data analytics engines like Hadoop and Spark with ease.
Features of Amazon EBS
It offers the following features:
Several volume kinds: Amazon EBS offers a variety of volume types that let you maximize storage efficiency and affordability for a wide range of uses. There are two main sorts of volume types: HDD-backed storage for workloads requiring high throughput and SSD-backed storage for transactional workloads.
Scalability: You can build Amazon EBS volumes with the performance and capacity requirements you want. You may adjust performance or dynamically expand capacity using Elastic Volumes operations as your needs change, all without any downtime.
Recovery and backup: Back up the data on your disks using Amazon EBS snapshots. Those snapshots can subsequently be used to transfer data between AWS accounts, AWS Regions, or Availability Zones or to restore volumes instantaneously.
Data protection: Encrypt your Amazon EBS volumes and snapshots using Amazon EBS encryption. To secure data-at-rest and data-in-transit between an instance and its connected volume and subsequent snapshots, encryption procedures are carried out on the servers that house Amazon EC2 instances.
Data availability and durability: io2 Block Express volumes have an annual failure rate of 0.001% and a durability of 99.999%. With a 0.1% to 0.2% yearly failure rate, other volume types offer endurance of 99.8% to 99.9%. To further guard against data loss due to a single component failure, volume data is automatically replicated across several servers in an Availability Zone.
Data archiving: EBS Snapshots Archive provides an affordable storage tier for storing full, point-in-time copies of EBS Snapshots, which you must maintain for a minimum of ninety days in order to comply with regulations. and regulatory purposes, or for upcoming project releases.
Related services
These services are compatible with Amazon EBS:
In the AWS Cloud, Amazon Elastic Compute Cloud lets you start and control virtual machines, or EC2 instances. Like hard drives, EBS volumes may store data and install software.
You can produce and maintain cryptographic keys with AWS Key Management Service, a managed service. Data saved on your Amazon EBS volumes and in your Amazon EBS snapshots can be encrypted using AWS KMS cryptographic keys.
EBS snapshots and AMIs supported by EBS are automatically created, stored, and deleted with Amazon Data Lifecycle Manager, a managed service. Backups of your Amazon EC2 instances and Amazon EBS volumes can be automated with Amazon Data Lifecycle Manager.
EBS direct APIs: These services let you take EBS snapshots, write data to them directly, read data from them, and determine how two snapshots differ or change from one another.
Recycle Bin is a data recovery solution that lets you recover EBS-backed AMIs and mistakenly erased EBS snapshots.
Accessing Amazon EBS
The following interfaces are used to build and manage your Amazon EBS resources:
Amazon EC2 console
A web interface for managing and creating snapshots and volumes.
AWS Command Line Interface
A command-line utility that enables you to use commands in your command-line shell to control Amazon EBS resources. Linux, Mac, and Windows are all compatible.
AWS Tools for PowerShell
A set of PowerShell modules for scripting Amazon EBS resource activities from the command line.
Amazon CloudFormation
It’s a fully managed AWS service that allows you describe your AWS resources using reusable JSON or YAML templates, and then it will provision and setup those resources for you.
Amazon EC2 Query API
The HTTP verbs GET or POST and a query parameter called Action are used in HTTP or HTTPS requests made through the Amazon EC2 Query API.
Amazon SDKs
APIs tailored to particular languages that let you create apps that interface with AWS services. Numerous well-known programming languages have AWS SDKs available.
Amazon EBS Pricing
You just pay for what you provision using Amazon EBS. See Amazon EBS pricing for further details.
Read more on Govindhtech.com
#AmazonEBS#ElasticBlockStore#AmazonEC2#EBSvolumes#EC2instances#EBSSnapshots#News#Technews#Technology#Technologynews#Technologytrends#Govindhtech
0 notes
Text
In the dynamic world of data science, staying updated with the latest tools and applications is crucial. These tools not only enhance productivity but also streamline complex workflows, allowing data scientists to focus on deriving insights and making informed decisions. Here’s a comprehensive guide to some of the best tools and apps that every data scientist should have in their arsenal. 1. Jupyter Notebook Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It supports over 40 programming languages, including Python, R, and Julia. Jupyter is particularly useful for data cleaning and transformation, numerical simulation, statistical modeling, data visualization, and machine learning. Key Features: Interactive output that supports various visualizations. Integration with big data tools like Apache Spark. Extensibility through plugins and extensions. 2. Anaconda Anaconda is a distribution of Python and R for scientific computing and data science. It simplifies package management and deployment, making it easier to manage libraries and dependencies. Anaconda includes popular data science packages and tools, such as Jupyter, pandas, and scikit-learn. Key Features: Conda package manager for seamless installation and management of packages. Anaconda Navigator, a graphical interface to manage environments and launch applications. Built-in Jupyter and RStudio for comprehensive data analysis and visualization. 3. TensorFlow TensorFlow is an open-source machine learning library developed by Google. It is widely used for building and training neural networks, with a focus on deep learning. TensorFlow offers flexible deployment options and extensive support for various platforms, including desktops, mobile devices, and servers. Key Features: High-level APIs such as Keras for easy model building. TensorFlow Serving for deploying machine learning models in production environments. TensorBoard for visualizing the training process and metrics. 4. Tableau Tableau is a powerful data visualization tool that helps data scientists and analysts to see and understand their data. It allows users to create a wide range of visualizations to interactively explore and analyze data. Tableau supports various data sources, including spreadsheets, databases, and cloud services. Key Features: Drag-and-drop interface for creating interactive dashboards. Real-time collaboration and sharing capabilities. Extensive library of visualization types and customization options. 5. PyCharm PyCharm is an Integrated Development Environment (IDE) for Python, developed by JetBrains. It provides a robust environment for coding, debugging, and testing Python applications. PyCharm is particularly useful for data scientists working with Python-based data analysis and machine learning projects. Key Features: Intelligent code editor with code completion and error highlighting. Integrated tools for debugging, testing, and version control. Support for Jupyter Notebook integration. 6. Apache Spark Apache Spark is an open-source distributed computing system that provides an interface for programming entire clusters with implicit data parallelism and fault tolerance. Spark is known for its speed and efficiency in processing large-scale data, making it a popular choice for big data analytics. Key Features: In-memory computing capabilities for faster data processing. Support for SQL queries, streaming data, and machine learning. Integration with Hadoop and other big data tools. 7. GitHub GitHub is a web-based platform used for version control and collaborative software development. It is essential for data scientists to manage their codebase, collaborate with team members, and track changes efficiently. GitHub also provides hosting for software development and a collaborative environment through its Git repositories.
Key Features: Branching and merging for parallel development. Issue tracking and project management tools. Integration with CI/CD pipelines for automated testing and deployment. 8. RStudio RStudio is an IDE for R, a programming language widely used for statistical computing and graphics. RStudio provides a user-friendly interface to work with R and supports a wide range of statistical and graphical techniques. Key Features: Code editor with syntax highlighting and code completion. Integrated tools for plotting, history, and workspace management. Support for R Markdown for creating dynamic reports. 9. Docker Docker is a platform for developing, shipping, and running applications in containers. Containers allow data scientists to package their applications and dependencies into a single, portable unit that can run consistently across different computing environments. Key Features: Isolation of applications and dependencies. Scalability and flexibility in deploying applications. Support for Docker Compose to manage multi-container applications. 10. KNIME KNIME (Konstanz Information Miner) is an open-source data analytics, reporting, and integration platform. It is designed to provide a comprehensive solution for data preprocessing, analysis, and visualization through a modular, workflow-based approach. Key Features: Drag-and-drop interface for creating data workflows. Integration with various data sources and machine learning libraries. Community extensions for additional functionalities. Conclusion Equipping yourself with the right tools and apps can significantly enhance your productivity and efficiency as a data scientist. From data cleaning and visualization to machine learning and deployment, these tools cover a wide spectrum of data science needs. Staying updated with these essential tools will not only streamline your workflow but also help you stay ahead in the ever-evolving field of data science.
0 notes
Text
Unlocking Data's Potential with SQL Server Standard 2019
Empowering Your Data Future with Cutting-Edge Technology
In today’s rapidly evolving digital landscape, harnessing the full potential of your data is more critical than ever. SQL Server Standard 2019 stands at the forefront of data management solutions, offering a robust platform that combines performance, security, and scalability. This version introduces innovative features that transform raw data into strategic insights, propelling businesses towards a smarter future.
One of the key advantages of SQL Server Standard 2019 is its enhanced support for hybrid cloud environments. Organizations can seamlessly integrate on-premises data with cloud services, ensuring flexibility and resilience. This hybrid approach not only optimizes resource utilization but also enables real-time data analysis, critical for making swift, informed decisions.
Security remains a top priority with SQL Server 2019. It incorporates advanced security features like data encryption, vulnerability assessments, and threat detection, safeguarding sensitive information against evolving cyber threats. Whether you are managing customer data or proprietary business information, these security enhancements provide peace of mind.
Furthermore, SQL Server Standard 2019 empowers data professionals with improved performance and scalability. Its intelligent query processing engine accelerates data retrieval, while support for large datasets ensures your infrastructure can grow without compromise. This scalability is vital for businesses aiming to stay ahead in competitive markets.
Business intelligence and analytics capabilities are significantly boosted with built-in support for big data clusters and data virtualization. These features facilitate comprehensive data analysis, enabling organizations to uncover hidden patterns and gain actionable insights. For a detailed overview, explore The Future of Your Data: Harnessing the Power of SQL Server Standard 2019.
Transitioning to SQL Server Standard 2019 is not just about upgrading technology; it’s about transforming your data into a strategic asset. With its innovative features, security enhancements, and flexible deployment options, this platform prepares your organization for the challenges and opportunities of tomorrow.
In conclusion, embracing SQL Server Standard 2019 paves the way for a future where data drives innovation, efficiency, and growth. Stay ahead of the curve by leveraging its powerful capabilities and secure architecture. Your data’s potential is limitless—unlock it today and step confidently into the future.
#SQL Server 2019#Data Management#Business Intelligence#Data Security#Cloud Integration#Data Analytics#Enterprise Data Platform
0 notes
Text
Mastering Database Administration with Your Path to Expert DB Management

In the age of data-driven businesses, managing and securing databases has never been more crucial. A database administrator (DBA) is responsible for ensuring that databases are well-structured, secure, and perform optimally. Whether you're dealing with a small-scale application or a large enterprise system, the role of a database administrator is key to maintaining data integrity, availability, and security.
If you're looking to build a career in database administration or enhance your existing skills, Jazinfotech’s Database Administration course offers comprehensive training that equips you with the knowledge and hands-on experience to manage databases efficiently and effectively.
In this blog, we’ll explore what database administration entails, why it's an essential skill in today's tech industry, and how Jazinfotech’s can help you become an expert in managing and maintaining databases for various platforms.
1. What is Database Administration (DBA)?
Database Administration refers to the practice of managing, configuring, securing, and maintaining databases to ensure their optimal performance. Database administrators are responsible for the overall health of the database environment, including aspects such as:
Data Security: Ensuring data is protected from unauthorized access and data breaches.
Database Performance: Monitoring and optimizing the performance of database systems to ensure fast and efficient data retrieval.
Backup and Recovery: Implementing robust backup strategies and ensuring databases can be restored in case of failures.
High Availability: Ensuring that databases are always available and accessible, even in the event of system failures.
Data Integrity: Ensuring that data remains consistent, accurate, and reliable across all operations.
Database administrators work with various types of databases (SQL, NoSQL, cloud databases, etc.), and they often specialize in specific database management systems (DBMS) such as MySQL, PostgreSQL, Oracle, Microsoft SQL Server, and MongoDB.
2. Why is Database Administration Important?
Database administration is a critical aspect of managing the infrastructure of modern organizations. Here are some reasons why database administration is vital:
a. Ensures Data Security and Compliance
In today’s world, where data breaches and cyber threats are prevalent, ensuring that your databases are secure is essential. A skilled DBA implements robust security measures such as encryption, access control, and monitoring to safeguard sensitive information. Moreover, DBAs are responsible for ensuring that databases comply with various industry regulations and data privacy laws.
b. Optimizes Performance and Scalability
As organizations grow, so does the volume of data. A good DBA ensures that databases are scalable, can handle large data loads, and perform efficiently even during peak usage. Performance optimization techniques like indexing, query optimization, and database tuning are essential to maintaining smooth database operations.
c. Prevents Data Loss
Data is often the most valuable asset for businesses. DBAs implement comprehensive backup and disaster recovery strategies to prevent data loss due to system crashes, human error, or cyber-attacks. Regular backups and recovery drills ensure that data can be restored quickly and accurately.
d. Ensures High Availability
Downtime can have significant business impacts, including loss of revenue, user dissatisfaction, and brand damage. DBAs design high-availability solutions such as replication, clustering, and failover mechanisms to ensure that the database is always accessible, even during maintenance or in case of failures.
e. Supports Database Innovation
With the evolution of cloud platforms, machine learning, and big data technologies, DBAs are also involved in helping organizations adopt new database technologies. They assist with migration to the cloud, implement data warehousing solutions, and work on database automation to support agile development practices.
3. Jazinfotech’s Database Administration Course: What You’ll Learn
At Jazinfotech, our Database Administration (DBA) course is designed to give you a thorough understanding of the core concepts and techniques needed to become an expert in database management. Our course covers various DBMS technologies, including SQL and NoSQL databases, and teaches you the necessary skills to manage databases effectively and efficiently.
Here’s a breakdown of the core topics you’ll cover in Jazinfotech’s DBA course:
a. Introduction to Database Management Systems
Understanding the role of DBMS in modern IT environments.
Types of databases: Relational, NoSQL, NewSQL, etc.
Key database concepts like tables, schemas, queries, and relationships.
Overview of popular DBMS technologies: MySQL, Oracle, SQL Server, PostgreSQL, MongoDB, and more.
b. SQL and Query Optimization
Mastering SQL queries to interact with relational databases.
Writing complex SQL queries: Joins, subqueries, aggregations, etc.
Optimizing SQL queries for performance: Indexing, query execution plans, and normalization.
Data integrity and constraints: Primary keys, foreign keys, and unique constraints.
c. Database Security and User Management
Implementing user authentication and access control.
Configuring database roles and permissions to ensure secure access.
Encryption techniques for securing sensitive data.
Auditing database activity and monitoring for unauthorized access.
d. Backup, Recovery, and Disaster Recovery
Designing a robust backup strategy (full, incremental, differential backups).
Automating backup processes to ensure regular and secure backups.
Recovering data from backups in the event of system failure or data corruption.
Implementing disaster recovery plans for business continuity.
e. Database Performance Tuning
Monitoring and analyzing database performance.
Identifying performance bottlenecks and implementing solutions.
Optimizing queries, indexing, and database configuration.
Using tools like EXPLAIN (for query analysis) and performance_schema to improve DB performance.
f. High Availability and Replication
Setting up database replication (master-slave, master-master) to ensure data availability.
Designing high-availability database clusters to prevent downtime.
Load balancing to distribute database requests and reduce the load on individual servers.
Failover mechanisms to automatically switch to backup systems in case of a failure.
g. Cloud Database Administration
Introduction to cloud-based database management systems (DBaaS) like AWS RDS, Azure SQL, and Google Cloud SQL.
Migrating on-premise databases to the cloud.
Managing database instances in the cloud, including scaling and cost management.
Cloud-native database architecture for high scalability and resilience.
h. NoSQL Database Administration
Introduction to NoSQL databases (MongoDB, Cassandra, Redis, etc.).
Managing and scaling NoSQL databases.
Differences between relational and NoSQL data models.
Querying and optimizing performance for NoSQL databases.
i. Database Automation and Scripting
Automating routine database maintenance tasks using scripts.
Scheduling automated backups, cleanup jobs, and index maintenance.
Using Bash, PowerShell, or other scripting languages for database automation.
4. Why Choose Jazinfotech for Your Database Administration Course?
At Jazinfotech, we provide high-quality, practical training in database administration. Our comprehensive DBA course covers all aspects of database management, from installation and configuration to performance tuning and troubleshooting.
Here’s why you should choose Jazinfotech for your DBA training:
a. Experienced Trainers
Our instructors are seasoned database professionals with years of hands-on experience in managing and optimizing databases for enterprises. They bring real-world knowledge and industry insights to the classroom, ensuring that you learn not just theory, but practical skills.
b. Hands-On Training
Our course offers plenty of hands-on labs and practical exercises, allowing you to apply the concepts learned in real-life scenarios. You will work on projects that simulate actual DBA tasks, including performance tuning, backup and recovery, and database security.
c. Industry-Standard Tools and Technologies
We teach you how to work with the latest database tools and technologies, including both relational and NoSQL databases. Whether you're working with Oracle, SQL Server, MySQL, MongoDB, or cloud-based databases like AWS RDS, you'll gain the skills needed to manage any database environment.
d. Flexible Learning Options
We offer both online and in-person training options, making it easier for you to learn at your own pace and according to your schedule. Whether you prefer classroom-based learning or virtual classes, we have the right solution for you.
e. Career Support and Placement Assistance
At Jazinfotech, we understand the importance of securing a job after completing the course. That’s why we offer career support and placement assistance to help you find your next role as a Database Administrator. We provide resume-building tips, mock interviews, and help you connect with potential employers.
5. Conclusion
Database administration is a critical skill that ensures your organization’s data is secure, accessible, and performant. With the right training and experience, you can become a highly skilled database administrator and take your career to new heights.
Jazinfotech’s Database Administration course provides the comprehensive knowledge, hands-on experience, and industry insights needed to excel in the field of database management. Whether you’re a beginner looking to start your career in database administration or an experienced professional aiming to deepen your skills, our course will help you become a proficient DBA capable of managing complex database environments.
Ready to kickstart your career as a Database Administrator? Enroll in Jazinfotech’s DBA course today and gain the expertise to manage and optimize databases for businesses of all sizes!
0 notes
Text
The Significance of Scalable NoSQL Databases in Contemporary Applications
In the era of big data, businesses encounter unprecedented demands for the processing, storage, and management of vast amounts of information in real-time. Traditional relational databases (SQL) often struggle to meet the evolving needs of modern applications, particularly regarding scalability and flexibility. This is where NoSQL databases prove invaluable, offering a versatile solution for businesses that require high performance, scalability, and flexibility in managing unstructured or semi-structured data.
NoSQL databases have become essential tools for contemporary, data-intensive applications, especially those operating in dynamic environments such as e-commerce, social networks, IoT (Internet of Things), and cloud-native applications. In this blog post, we will examine the advantages of scalable NoSQL databases and how they effectively address the challenges of today's data-centric landscape.
What is a NoSQL Database?
NoSQL, or "Not Only SQL," encompasses a range of non-relational database technologies designed to manage large volumes of data that may not conform to the strict structures of traditional relational databases. Unlike conventional SQL databases, which organize data in rows and tables, NoSQL databases utilize more flexible formats, including documents, graphs, key-value pairs, and wide-column stores.
Some of the most widely used NoSQL databases include MongoDB, Cassandra, Redis, Couchbase, and Amazon DynamoDB. These databases are engineered for horizontal scalability, making them highly effective at managing extensive datasets distributed across multiple servers.
Why Scalability Matters
Scalability is a crucial consideration for any database system employed in contemporary applications, particularly for organizations confronting substantial datasets or fluctuations in traffic. NoSQL databases provide horizontal scalability, enabling businesses to distribute their data across multiple servers or clusters in response to increasing demand. This approach ensures that the database can accommodate heightened workloads without compromising performance.
In contrast, traditional SQL databases generally rely on vertical scalability, which entails augmenting resources (CPU, memory, storage) on a single server. However, vertical scaling has inherent limitations and can become costly and less efficient as datasets expand. This is where NoSQL databases truly excel.
Benefits of Scalable NoSQL Databases
Horizontal Scalability
NoSQL databases are engineered to scale horizontally by distributing data across multiple servers or nodes. This capability presents a significant advantage for applications that must manage large datasets or high traffic volumes, as horizontal scaling allows for the addition of servers in response to data growth, thereby ensuring consistent performance. For instance, platforms such as Facebook and Twitter, which handle millions of users and real-time updates, leverage NoSQL databases like Cassandra to achieve horizontal scalability, enabling them to process vast amounts of unstructured data without encountering performance bottlenecks.
Flexibility and Schema-Less Design
NoSQL databases provide enhanced flexibility regarding data storage. Unlike SQL databases that necessitate a predefined schema, NoSQL databases can accommodate data in various formats, such as JSON, XML, or key-value pairs. This schema-less characteristic empowers developers to modify and adapt the data structure as applications evolve, facilitating the management of diverse data types without the need to alter the underlying database structure.
High Performance for Big Data
Scalable NoSQL databases are optimized for big data handling, offering accelerated read and write operations critical for real-time applications where response times are crucial. By dispersing data across multiple nodes, NoSQL databases mitigate bottlenecks and ensure that data retrieval and processing occur concurrently across different servers. For example, e-commerce giants like Amazon utilize NoSQL databases to manage customer orders, inventory, and transactions, guaranteeing smooth and prompt responses even during peak sales periods.
Cost-Effective Scaling
NoSQL databases offer horizontal scalability, enabling businesses to expand their storage and computing capacity by adding commodity servers rather than investing in costly, high-performance machines. This distributed architecture renders NoSQL databases more cost-effective for large-scale applications, allowing organizations to scale in alignment with their needs without incurring significant capital expenditures.
Handling Unstructured Data
Modern applications generate a wide array of unstructured or semi-structured data types, including social media interactions, sensor data, multimedia content, and logs. NoSQL databases are specifically designed to accommodate this diversity, making them an ideal choice for IoT applications, real-time analytics, and content management systems.
Use Cases for Scalable NoSQL Databases
E-commerce Platforms: With rapidly changing inventory, customer data, and transactional histories, e-commerce platforms require databases that can scale swiftly while delivering real-time analytics. NoSQL solutions such as MongoDB and Cassandra provide the scalability and performance necessary to maintain smooth operations, even during peak traffic events like Black Friday.
Social Networks: Applications like Instagram and Twitter must process vast amounts of data from users in real-time. NoSQL databases can efficiently store and retrieve this data without compromising the user experience, even as the network expands to accommodate millions of users.
IoT and Big Data: IoT devices generate massive data streams that need to be processed and analyzed in real-time. Scalable NoSQL databases can ingest, store, and analyze this data, delivering faster insights and promoting more efficient operations.
Conclusion
In today's landscape, where businesses must process extensive volumes of diverse data in real-time, scalable NoSQL databases offer the flexibility, performance, and cost-effective scalability essential to address these challenges. By enabling horizontal scaling, accommodating unstructured data, and providing high performance for big data applications, NoSQL databases are crucial for any organization aiming to remain competitive in the digital era. Whether developing a cloud-native application, an IoT platform, or a data-intensive service, a scalable NoSQL database is pivotal for fostering innovation and driving growth.
0 notes
Text
Top In-Demand Skills – Software Developers
Join the newsletter: https://avocode.digital/newsletter/
Introduction
In the dynamic world of technology, the role of software developers has become increasingly pivotal. As the industry evolves, so do the skills required to stay competitive. With businesses relying heavily on digital solutions, software developers are expected to be proficient in a wide array of competencies. This article explores the top in-demand skills for software developers in today's tech-centric environment.
1. Programming Languages
Python
Python continues to dominate the programming landscape owing to its versatility and simplicity. Whether it's web development, data science, or artificial intelligence, Python's extensive libraries and frameworks make it indispensable.
JavaScript
JavaScript remains a cornerstone for any front-end developer. Its prevalence in web development, along with frameworks like React, Angular, and Vue.js, ensures that it remains one of the most sought-after skills.
SQL
As businesses accumulate vast amounts of data, SQL (Structured Query Language) is crucial for database management and manipulation. It enables developers to retrieve and manipulate data efficiently, making it essential for backend development and data analytics.
2. Web Development Frameworks
React.js
React.js, a library developed by Facebook, has revolutionized the way developers build user interfaces. Its component-based architecture allows for reusable UI components, enhancing the development speed and maintainability of web applications.
Node.js
Node.js allows JavaScript to be used for server-side scripting, running scripts server-side to produce dynamic web page content before the page is sent to the user's web browser. This makes it a valued skill for full-stack developers.
3. Cloud Computing
Cloud computing has radically transformed how applications are deployed and managed. As companies migrate to the cloud, skills in platforms like AWS (Amazon Web Services), Azure, and Google Cloud are in high demand.
AWS - Widely regarded as the leading cloud service provider, knowledge of AWS cloud services, like EC2 and S3, is crucial for modern software development.
Azure - With seamless integration into the existing Microsoft ecosystem, Azure is a preferred cloud solution for many businesses.
Google Cloud – Known for its big data analytics and machine learning services, Google Cloud is essential for companies focused on innovation.
4. Containerization and Orchestration
Containerization tools, such as Docker, and orchestration platforms, like Kubernetes, have become vital in the development and deployment of applications. They ensure consistency, scalability, and reliability across different environments.
Docker - Enables developers to package applications and their dependencies into containers that can run consistently across various environments.
Kubernetes - An open-source platform designed to automate deploying, scaling, and operating application containers, it helps manage containerized applications in a clustered environment.
5. DevOps
DevOps practices bridge the gap between development and operations, fostering a culture of collaboration and continuous improvement. Proficiency in CI/CD (Continuous Integration/Continuous Deployment) tools such as Jenkins, GitLab CI, and CircleCI is highly valued.
Jenkins - An open-source automation server that supports building, deploying, and automating any project.
GitLab CI - A built-in CI/CD system to manage the life cycle of projects and ensure continuous deployment.
CircleCI – Known for performance and scalability, it automates the software development process using continuous integration and delivery.
6. Data Analytics and Machine Learning
Data Analytics
As data becomes a critical asset for businesses, skills in data analytics enable developers to derive actionable insights. Proficiency in tools like Apache Spark, Hadoop, and Tableau facilitates effective data processing and visualization.
Machine Learning
Machine learning is revolutionizing numerous sectors by enabling systems to learn and make decisions with minimal human intervention. Skills in machine learning libraries like TensorFlow, PyTorch, and scikit-learn are highly sought after.
7. Cybersecurity
With the increasing frequency of cyber-attacks, cybersecurity has become a top priority. Developers must be adept at identifying vulnerabilities, securing application code, and implementing robust security measures.
Ethical Hacking - Understanding common vulnerabilities and penetration testing helps in identifying and mitigating security risks.
Encryption - Techniques like SSL/TLS and hashing ensure data privacy and integrity.
Security Protocols - Knowledge of security protocols such as OAuth, JWT, and SAML is crucial for secure authentication and authorization.
8. Soft Skills
In addition to technical proficiency, developers must also hone their soft skills to enhance teamwork and productivity.
Communication - Clear and effective communication is vital for collaborative development and project success.
Problem-Solving - The ability to troubleshoot and resolve issues quickly is essential for maintaining project timelines.
Adaptability - The tech landscape is ever-evolving, and developers need to be adaptable to new tools and technologies.
Conclusion
The technology sector is moving at a breakneck pace, and the demand for skilled software developers is higher than ever. By mastering these in-demand skills, developers can not only stay relevant but also excel in their careers. From mastering key programming languages like Python and JavaScript to embracing cloud computing and cybersecurity, today's developers need a diverse skill set to thrive in this ever-changing industry. Staying updated with the latest trends and continuously learning are crucial for a successful career in software development. Investing in these skills will undoubtedly open up a plethora of opportunities in the tech world. Want more? Join the newsletter: https://avocode.digital/newsletter/
0 notes
Text
Unleashing the Power of Big Data | Apache Spark Implementation & Consulting Services
In today’s data-driven world, businesses are increasingly relying on robust technologies to process and analyze vast amounts of data efficiently. Apache Spark stands out as a powerful, open-source unified analytics engine designed for large-scale data processing. Its capability to handle real-time data processing, complex analytics, and machine learning makes it an invaluable tool for organizations aiming to gain actionable insights from their data. At Feathersoft, we offer top-tier Apache Spark implementation and consulting services to help you harness the full potential of this transformative technology.
Why Apache Spark?
Apache Spark is renowned for its speed and versatility. Unlike traditional data processing frameworks that rely heavily on disk storage, Spark performs in-memory computations, which significantly boosts processing speed. Its ability to handle both batch and real-time processing makes it a versatile choice for various data workloads. Key features of Apache Spark include:
In-Memory Computing: Accelerates data processing by storing intermediate data in memory, reducing the need for disk I/O.
Real-Time Stream Processing: Processes streaming data in real-time, providing timely insights and enabling quick decision-making.
Advanced Analytics: Supports advanced analytics, including machine learning, graph processing, and SQL-based queries.
Scalability: Easily scales from a single server to thousands of machines, making it suitable for large-scale data processing.
Our Apache Spark Implementation Services
Implementing Apache Spark can be complex, requiring careful planning and expertise. At Feathersoft, we provide comprehensive Apache Spark implementation services tailored to your specific needs. Our services include:
Initial Assessment and Strategy Development: We start by understanding your business goals, data requirements, and existing infrastructure. Our team develops a detailed strategy to align Spark’s capabilities with your objectives.
Custom Solution Design: Based on your requirements, we design a custom Apache Spark solution that integrates seamlessly with your data sources and analytics platforms.
Implementation and Integration: Our experts handle the end-to-end implementation of Apache Spark, ensuring smooth integration with your existing systems. We configure Spark clusters, set up data pipelines, and optimize performance for efficient processing.
Performance Tuning: To maximize Spark’s performance, we perform extensive tuning and optimization, addressing any bottlenecks and ensuring your system operates at peak efficiency.
Training and Support: We offer training sessions for your team to get acquainted with Apache Spark’s features and capabilities. Additionally, our support services ensure that you receive ongoing assistance and maintenance.
Why Choose Us?
At Feathersoft, we pride ourselves on delivering exceptional Apache Spark consulting services. Here’s why businesses trust us:
Expertise: Our team comprises seasoned professionals with extensive experience in Apache Spark implementation and consulting.
Tailored Solutions: We provide customized solutions that cater to your unique business needs and objectives.
Proven Track Record: We have a history of successful Apache Spark projects across various industries, demonstrating our capability to handle diverse requirements.
Ongoing Support: We offer continuous support to ensure the smooth operation of your Spark environment and to address any issues promptly.
Conclusion
Apache Spark is a game-changer in the realm of big data analytics, offering unprecedented speed and flexibility. With our Apache Spark implementation and consulting services, Feathersoft can help you leverage this powerful technology to drive data-driven decision-making and gain a competitive edge. Contact us today to explore how Apache Spark can transform your data strategy.
#ApacheSpark#BigData#DataAnalytics#RealTimeData#DataProcessing#MachineLearning#DataScience#SparkConsulting#DataEngineering#InMemoryComputing#TechConsulting#DataSolutions#BigDataAnalytics#SparkImplementation#TechServices#DataStrategy#AnalyticsConsulting#CloudData#DataOptimization#SparkExperts
0 notes
Text
Top Google Cloud Platform Development Services
Google Cloud Platform Development Services encompass a broad range of cloud computing services provided by Google, designed to enable developers to build, deploy, and manage applications on Google's highly scalable and reliable infrastructure. GCP offers an extensive suite of tools and services specifically designed to meet diverse development needs, ranging from computing, storage, and databases to machine learning, artificial intelligence, and the Internet of Things (IoT).
Core Components of GCP Development Services
Compute Services: GCP provides various computing options like Google Compute Engine (IaaS), Google Kubernetes Engine (GKE), App Engine (PaaS), and Cloud Functions (serverless computing). These services cater to different deployment scenarios and scalability requirements, ensuring developers have the right tools for their specific needs.
Storage and Database Services: GCP offers a comprehensive array of storage solutions, including Google Cloud Storage for unstructured data, Cloud SQL and Cloud Spanner for relational databases, and Bigtable for NoSQL databases. These services provide scalable, durable, and highly available storage options for any application.
Networking: GCP's networking services, such as Cloud Load Balancing, Cloud CDN, and Virtual Private Cloud (VPC), ensure secure, efficient, and reliable connectivity and data transfer. These tools help optimize performance and security for applications hosted on GCP.
Big Data and Analytics: Tools like BigQuery, Cloud Dataflow, and Dataproc facilitate large-scale data processing, analysis, and machine learning. These services empower businesses to derive actionable insights from their data, driving informed decision-making and innovation.
AI and Machine Learning: GCP provides advanced AI and ML services such as TensorFlow, Cloud AI, and AutoML, enabling developers to build, train, and deploy sophisticated machine learning models with ease.
Security: GCP includes robust security features like Identity and Access Management (IAM), Cloud Security Command Center, and encryption at rest and in transit. These tools help protect data and applications from unauthorized access and potential threats.
Latest Tools Used in Google Cloud Platform Development Services
Anthos: Anthos is a hybrid and multi-cloud platform that allows developers to build and manage applications consistently across on-premises and cloud environments. It provides a unified platform for managing clusters and services, enabling seamless application deployment and management.
Cloud Run: Cloud Run is a fully managed serverless platform that allows developers to run containers directly on GCP without managing the underlying infrastructure. It supports any containerized application, making it easy to deploy and scale services.
Firestore: Firestore is a NoSQL document database that simplifies the development of serverless applications. It offers real-time synchronization, offline support, and seamless integration with other GCP services.
Cloud Build: Cloud Build is a continuous integration and continuous delivery (CI/CD) tool that automates the building, testing, and deployment of applications. It ensures faster, more reliable software releases by streamlining the development workflow.
Vertex AI: Vertex AI is a managed machine learning platform that provides the tools and infrastructure necessary to build, deploy, and scale AI models efficiently. It integrates seamlessly with other GCP services, making it a powerful tool for AI development.
Cloud Functions: Cloud Functions is a serverless execution environment that allows developers to run code in response to events without provisioning or managing servers. It supports various triggers, including HTTP requests, Pub/Sub messages, and database changes.
Importance of Google Cloud Platform Development Services for Secure Data and Maintenance
Enhanced Security: GCP employs advanced security measures, including encryption at rest and in transit, identity management, and robust access controls. These features ensure that data is protected against unauthorized access and breaches, making GCP a secure choice for sensitive data.
Compliance and Certifications: GCP complies with various industry standards and regulations, such as GDPR, HIPAA, and ISO/IEC 27001. This compliance provides businesses with the assurance that their data handling practices meet stringent legal requirements.
Reliability and Availability: GCP's global infrastructure and redundant data centers ensure high availability and reliability. Services like Cloud Load Balancing and auto-scaling maintain performance and uptime even during traffic spikes, ensuring continuous availability of applications.
Data Management: GCP offers a range of tools for efficient data management, including Cloud Storage, BigQuery, and Dataflow. These services enable businesses to store, process, and analyze vast amounts of data seamlessly, driving insights and innovation.
Disaster Recovery: GCP provides comprehensive disaster recovery solutions, including automated backups, data replication, and recovery testing. These features minimize data loss and downtime during unexpected events, ensuring business continuity.
Why Shilsha Technologies is the Best Company for Google Cloud Platform Development Services in India
Expertise and Experience: Shilsha Technologies boasts a team of certified GCP experts with extensive experience in developing and managing cloud solutions. Their deep understanding of GCP ensures that clients receive top-notch services customized to your requirements.
Comprehensive Services: From cloud migration and application development to data analytics and AI/ML solutions, Shilsha Technologies offers a full spectrum of GCP services. This makes them a one-stop solution for all cloud development needs.
Customer-Centric Approach: Shilsha Technologies emphasizes a customer-first approach, ensuring that every project aligns with the client's business goals and delivers measurable value. It's their commitment to customer satisfaction that sets them apart from the competition.
Innovative Solutions: By leveraging the latest GCP tools and technologies, Shilsha Technologies delivers innovative and scalable solutions that drive business growth and operational efficiency.
Excellent Portfolio: With an excellent portfolio of successful projects across various industries, Shilsha Technologies has demonstrated its ability to deliver high-quality GCP solutions that meet and exceed client expectations.
How to Hire a Developer in India from Shilsha Technologies
Initial Consultation: Contact Shilsha Technologies through their website or customer service to discuss your project requirements and objectives. An initial consultation will help determine the scope of the project and the expertise needed.
Proposal and Agreement: Based on the consultation, Shilsha Technologies will provide a detailed proposal outlining the project plan, timeline, and cost. Contracts are signed once they have been agreed upon.
Team Allocation: Shilsha Technologies will assign a dedicated team of GCP developers and specialists customized to your project requirements. The team will include project managers, developers, and QA experts to ensure seamless project execution.
Project Kickoff: The project begins with a kickoff meeting to align the team with your goals and establish communication protocols. Regular updates and progress reports keep you informed throughout the development process.
Ongoing Support: After the project is completed, Shilsha Technologies offers ongoing support and maintenance services to ensure the continued success and optimal performance of your GCP solutions.
Google Cloud Platform Development Services provide robust, secure, and scalable cloud solutions, and Shilsha Technologies stands out as the premier Google Cloud Platform Development Company in India. By choosing Shilsha Technologies, businesses can harness the full potential of GCP to drive innovation and growth. So, if you're looking to hire a developer in India, Shilsha Technologies should be your top choice.
Source file
Reference: https://hirefulltimedeveloper.blogspot.com/2024/07/top-google-cloud-platform-development.html
#Hire Google Cloud Experts#Google Cloud Consulting Company#Google Cloud Development Company#Google Cloud Development Services#Google Cloud Platform Development Services
0 notes
Text
Hive Hadoop
Hive and Hadoop are essential components in big data and data analytics. Here’s an overview:
Hadoop: Hadoop is an open-source framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale from single servers to thousands of machines, each offering local computation and storage. The key components of Hadoop include:
Hadoop Distributed File System (HDFS): A distributed file system that provides high-throughput access to application data.
Hadoop YARN: A framework for job scheduling and cluster resource management.
Hadoop MapReduce: A YARN-based system for parallel processing of large data sets.
Hive: Hive is a data warehouse software project built on top of Hadoop to provide data querying and analysis. Hive gives an SQL-like interface to query data stored in various databases and file systems that integrate with Hadoop, including the HDFS, Apache HBase, and Amazon S3 filesystems. It is used for:
Data summarization, query, and analysis: Hive provides tools for easy data summarization, ad-hoc query, and analysis of large datasets stored in Hadoop-compatible file systems.
HiveQL: Hive defines a simple SQL-like query language, HiveQL, which can be used to query, summarize, explore, and analyze data. Under the hood, HiveQL queries are converted into a series of MapReduce, Tez, or Spark jobs for execution on Hadoop.
Both Hive and Hadoop are essential for handling big data, especially for large-scale data processing and analysis tasks. They are widely used in industries that handle large volumes of data, such as e-commerce, finance, telecommunications, and more. Their scalability and efficiency make them ideal for businesses leveraging data for strategic decision-making.
Hadoop Training Demo Day 1 Video:
youtube
You can find more information about Hadoop Training in this Hadoop Docs Link
Conclusion:
Unogeeks is the №1 IT Training Institute for Hadoop Training. Anyone Disagree? Please drop in a comment
You can check out our other latest blogs on Hadoop Training here — Hadoop Blogs
Please check out our Best In Class Hadoop Training Details here — Hadoop Training
S.W.ORG
— — — — — — — — — — — -
For Training inquiries:
Call/Whatsapp: +91 73960 33555
Mail us at: [email protected]
Our Website ➜ https://unogeeks.com
Follow us:
Instagram: https://www.instagram.com/unogeeks
Facebook: https://www.facebook.com/UnogeeksSoftwareTrainingInstitute
Twitter: https://twitter.com/unogeeks
#unogeeks #training #ittraining #unogeekstraining
0 notes