#DataLake

[kittu800]

[jasonhayesaqe]

90% of Businesses Waste Their Data—Learn How to Be in the Winning 10%

Businesses today are collecting more data than ever before. But are they storing and managing it in the most effective way? The right architecture can transform your data into a powerful business asset. That’s where Data Analytics Consulting Services play a key role—helping you make sense of your options and guiding you toward smarter decisions.

There are three major approaches companies use to manage and analyze their data—Data Warehouse, Data Lake, and Data Lakehouse. Each one offers something unique, but choosing the wrong one can create roadblocks instead of insights.

Let’s break them down simply.

What is a Data Warehouse?

Think of a Data Warehouse as a well-organized system built to store structured data—like sales records, customer info, and financial reports. It’s designed for fast reporting and decision-making.

Why use it?Because it delivers reliable, clean, and consistent data that powers dashboards, forecasts, and strategic planning.

What is a Data Lake?

A Data Lake holds everything—from structured files to raw, unstructured content like videos, images, logs, and more. It’s flexible, scalable, and great for storing large volumes of information in its native form.

Why use it?If your team is working with big data, machine learning, or predictive analytics, a Data Lake gives you room to explore and experiment.

What is a Data Lakehouse?

Here’s where things get interesting. A Data Lakehouse combines the storage flexibility of a Data Lake with the structure and analytics power of a Data Warehouse.

Why use it?Because it eliminates the need for two separate systems. With a Lakehouse, your business gets a unified solution that supports both advanced analytics and traditional reporting—saving time and cost.

So… Which One Should You Choose?

That’s the million-dollar question: Data Warehouse vs. Data Lake vs. Data Lakehouse—Which is the right approach for your business?

The answer isn’t simple. It depends on your goals, infrastructure, team capabilities, and future scalability plans. Choosing the wrong system could lead to unnecessary costs and missed insights. On the other hand, choosing the right one can completely transform how your business uses data.

Visit our detailed blog to discover the key differences, real-world use cases, and decision-making guide: 👉 Read Full Blog on Data Warehouse vs. Data Lake vs. Data Lakehouse

[excelworld]

🚀 What makes Delta Lake so powerful in a Lakehouse architecture? Delta Lake combines the reliability and performance of relational databases with the scalability and flexibility of data lakes. It's the best of both worlds — structured data management meets open data storage.

💡 Curious how this transforms your data strategy? Let’s discuss!

👇 Drop your thoughts in the comments.

[digitaleduskill]

How Azure Supports Big Data and Real-Time Data Processing

The explosion of digital data in recent years has pushed organizations to look for platforms that can handle massive datasets and real-time data streams efficiently. Microsoft Azure has emerged as a front-runner in this domain, offering robust services for big data analytics and real-time processing. Professionals looking to master this platform often pursue the Azure Data Engineering Certification, which helps them understand and implement data solutions that are both scalable and secure.

Azure not only offers storage and computing solutions but also integrates tools for ingestion, transformation, analytics, and visualization—making it a comprehensive platform for big data and real-time use cases.

Azure’s Approach to Big Data

Big data refers to extremely large datasets that cannot be processed using traditional data processing tools. Azure offers multiple services to manage, process, and analyze big data in a cost-effective and scalable manner.

1. Azure Data Lake Storage

Azure Data Lake Storage (ADLS) is designed specifically to handle massive amounts of structured and unstructured data. It supports high throughput and can manage petabytes of data efficiently. ADLS works seamlessly with analytics tools like Azure Synapse and Azure Databricks, making it a central storage hub for big data projects.

2. Azure Synapse Analytics

Azure Synapse combines big data and data warehousing capabilities into a single unified experience. It allows users to run complex SQL queries on large datasets and integrates with Apache Spark for more advanced analytics and machine learning workflows.

3. Azure Databricks

Built on Apache Spark, Azure Databricks provides a collaborative environment for data engineers and data scientists. It’s optimized for big data pipelines, allowing users to ingest, clean, and analyze data at scale.

Real-Time Data Processing on Azure

Real-time data processing allows businesses to make decisions instantly based on current data. Azure supports real-time analytics through a range of powerful services:

1. Azure Stream Analytics

This fully managed service processes real-time data streams from devices, sensors, applications, and social media. You can write SQL-like queries to analyze the data in real time and push results to dashboards or storage solutions.

2. Azure Event Hubs

Event Hubs can ingest millions of events per second, making it ideal for real-time analytics pipelines. It acts as a front-door for event streaming and integrates with Stream Analytics, Azure Functions, and Apache Kafka.

3. Azure IoT Hub

For businesses working with IoT devices, Azure IoT Hub enables the secure transmission and real-time analysis of data from edge devices to the cloud. It supports bi-directional communication and can trigger workflows based on event data.

Integration and Automation Tools

Azure ensures seamless integration between services for both batch and real-time processing. Tools like Azure Data Factory and Logic Apps help automate the flow of data across the platform.

Azure Data Factory: Ideal for building ETL (Extract, Transform, Load) pipelines. It moves data from sources like SQL, Blob Storage, or even on-prem systems into processing tools like Synapse or Databricks.

Logic Apps: Allows you to automate workflows across Azure services and third-party platforms. You can create triggers based on real-time events, reducing manual intervention.

Security and Compliance in Big Data Handling

Handling big data and real-time processing comes with its share of risks, especially concerning data privacy and compliance. Azure addresses this by providing:

Data encryption at rest and in transit

Role-based access control (RBAC)

Private endpoints and network security

Compliance with standards like GDPR, HIPAA, and ISO

These features ensure that organizations can maintain the integrity and confidentiality of their data, no matter the scale.

Career Opportunities in Azure Data Engineering

With Azure’s growing dominance in cloud computing and big data, the demand for skilled professionals is at an all-time high. Those holding an Azure Data Engineering Certification are well-positioned to take advantage of job roles such as:

Azure Data Engineer

Cloud Solutions Architect

Big Data Analyst

Real-Time Data Engineer

IoT Data Specialist

The certification equips individuals with knowledge of Azure services, big data tools, and data pipeline architecture—all essential for modern data roles.

Final Thoughts

Azure offers an end-to-end ecosystem for both big data analytics and real-time data processing. Whether it’s massive historical datasets or fast-moving event streams, Azure provides scalable, secure, and integrated tools to manage them all.

Pursuing an Azure Data Engineering Certification is a great step for anyone looking to work with cutting-edge cloud technologies in today’s data-driven world. By mastering Azure’s powerful toolset, professionals can design data solutions that are future-ready and impactful.

[innovaticsblog]

Optimize your data strategy by designing a data lake framework in AWS. Our guide provides expert advice on creating a scalable, efficient solution

[centizen]

Why Do So Many Big Data Projects Fail?

In our business analytics project work, we have often come in after several big data project failures of one kind or another. There are many reasons for this. They generally are not because of unproven technologies that were used because we have found that many new projects involving well-developed technologies fail. Why is this? Most surveys are quick to blame the scope, changing business requirements, lack of adequate skills etc. Based on our experience to date, we find that there are key attributes leading to successful big data initiatives that need to be carefully considered before you start a project. The understanding of these key attributes, below, will hopefully help you to avoid the most common pitfalls of big data projects.

Key attributes of successful Big Data projects

Develop a common understanding of what big data means for you

There is often a misconception of just what big data is about. Big data refers not just to the data but also the methodologies and technologies used to store and analyze the data. It is not simply “a lot of data”. It’s also not the size that counts but what you do with it. Understanding the definition and total scope of big data for your company is key to avoiding some of the most common errors that could occur.

Choose good use cases

Avoid choosing bad use cases by selecting specific and well defined use cases that solve real business problems and that your team already understand well. For example, a good use case could be that you want to improve the segmentation and targeting of specific marketing offers.

Prioritize what data and analytics you include in your analysis

Make sure that the data you’re collecting is the right data. Launching into a big data initiative with the idea that “We’ll just collect all the data that we can, and work out what to do with it later” often leads to disaster. Start with the data you already understand and flow that source of data into your data lake instead of flowing every possible source of data to the data lake.

Then next layer in one or two additional sources to enrich your analysis of web clickstream data or call centre text. Your cross-functional team can meet quarterly to prioritize and select the right use cases for implementation. Realize that it takes a lot of effort to import, clean and organize each data source.

Include non-data science subject matter experts (SMEs) in your team

Non-data science SMEs are the ones who understand their fields inside and out. They provide a context that allows you to understand what the data is saying. These SMEs are what frequently holds big data projects together. By offering on-the-job data science training to analysts in your organization interested in working in big data science, you will be able to far more efficiently fill project roles internally over hiring externally.

Ensure buy-in at all levels and good communication throughout the project

Big data projects need buy-in at every level, including senior leadership, middle management, nuts and bolts techies who will be carrying out the analytics and the workers themselves whose tasks will be affected by the results of the big data project. Everyone needs to understand what the big data project is doing and why? Not everyone needs to understand the ins and outs of the technical algorithms which may be running across the distributed, unstructured data that is analyzed in real time. But there should always be a logical, common-sense reason for what you are asking each member of the project team to do in the project. Good communication makes this happen.

Trust

All team members, data scientists and SMEs alike, must be able to trust each other. This is all about psychological safety and feeling empowered to contribute.

Summary

Big data initiatives executed well delivers significant and quantifiable business value to companies that take the extra time to plan, implement and roll out. Big data changes the strategy for data-driven businesses by overcoming barriers to analyzing large amounts of data, different types of unstructured and semi-structured data, and data that requires quick turnaround on results.

Being aware of the attributes of success above for big data projects would be a good start to making sure your big data project, whether it is your first or next one, delivers real business value and performance improvements to your organization.

[juveria-dalvi]

Data Lake VS Data Warehouse - Understanding the difference

Data Warehouse & Data Lake

Before we jump into discussing Data Warehouse & Data Lakes let us understand a little about Data. The term Data is all about information or we could say data & information are words that are used interchangeably, but there is still a difference between both of them. So what exactly does it mean ??

Data are "small chunks" of information that do not have value until and unless it is structured, but information is a set of Data that is addressing a value from the words itself.

Now that we understand the concept of Data, let's look forward to learning about Data Warehouse & Data Lake. From the name itself we could get the idea that there is data that is maintained like how people keep things in a warehouse, and how the rivers join together to meet and build a lake.

So to understand technically Data Warehouses & Data Lakes both of the terms are used to introduce the process of storing  Data.

Data Warehouse

A Data Warehouse is a storage place where different sets of databases are stored. Before the process of transferring data into a warehouse from any source or medium it is processed and cleaned and containerized into a database. It basically has summarized data which is later used for reporting and analytical purposes.

For an example, let us consider an e-commerce platform. They maintain a structured database containing customer details, product details, purchase history. This data is then cleaned, aggregated and organized in a data warehouse using ETL or ELT process.

Later this Data Warehouse is used to generate reports by analysts to make an informed data driven decision for a business.

Data Lake

A data lake is like a huge storage pool where you can dump all kinds of data—structured (like tables in a database), semi-structured (like JSON files), and unstructured (like images, videos, and text documents)—in their raw form, without worrying about organizing it first.

Imagine a Data Lake as a big, natural lake where you can pour in water from different sources— rivers, rain, streams, etc. Just like the water in a lake comes from different places and mixes together, a data lake stores all kinds of data from various sources.

Store Everything as It Is. In a data lake, you don’t need to clean, organize, or structure the data before storing it. You can just dump it in as it comes. This is useful because you might not know right away how you want to use the data, so you keep it all and figure that out later.

Since the data is stored in its raw form, you can later decide how to process or analyze it. Data scientists and analysts can use the data in whatever way they need, depending on the problem they’re trying to solve.

What is the connection between Data-warehouse and Data-lakes?

Data Lake: Think of it as the first stop for all your raw data. A data lake stores everything as it comes in—whether it’s structured, semi-structured, or unstructured—without much processing. It’s like a big, unfiltered collection of data from various sources.

Data Warehouse: After the data is in the lake, some of it is cleaned, organized, and transformed to make it more useful for analysis. This processed and structured data is then moved to a data warehouse, where it’s ready for specific business reports and queries

Together, they form a data ecosystem where the lake feeds into the warehouse, ensuring that raw data is preserved while also providing clean, actionable insights for the business.

[govindhtech]

AWS Supply Chain Features For Modernizing Your Operations

AWS Supply Chain Features

Description of the service

AWS Supply Chain integrates data and offers demand planning, integrated contextual collaboration, and actionable insights driven by machine learning.

Important aspects of the product

Data lakes

For supply chains to comprehend, retrieve, and convert heterogeneous, incompatible data into a single data model, AWS Supply Chain creates a data lake utilizing machine learning models. Data from a variety of sources, including supply chain management and ERP systems like SAP S/4HANA, can be ingested by the data lake.

AWS Supply Chain associates data from source systems to the unified data model using machine learning (ML) and natural language processing (NLP) in order to incorporate data from changeable sources like EDI 856. Predefined yet adaptable transformation procedures are used to directly transform EDI 850 and 860 messages. Amazon S3 buckets may also store data from other systems, which generative AI will map and absorb the AWS Supply Chain Data Lake.

Insights

Using the extensive supply chain data in the data lake, AWS Supply Chain automatically produces insights into possible supply chain hazards (such overstock or stock-outs) and displays them on an inventory visualization map. The inventory visualization map shows the quantity and selection of inventory that is currently available, together with the condition of each location’s inventory (e.g., inventory that is at risk of stock out).

Additionally, AWS Supply Chain provides work order analytics to show maintenance-related materials from sourcing to delivery, as well as order status, delivery risk identification, and delivery risk mitigation measures.

In order to produce more precise vendor lead-time forecasts, AWS Supply Chain uses machine learning models that are based on technology that is comparable to that used by Amazon. Supply planners can lower the risk of stock-outs or excess inventory by using these anticipated vendor lead times to adjust static assumptions included in planning models.

By choosing the location, risk type (such as stock-out or excess stock risk), and stock threshold, inventory managers, demand planners, and supply chain leaders can also make their own insight watchlists. They can then add team members as watchers. AWS Supply Chain will provide an alert outlining the possible risk and the affected locations if a risk is identified. Work order information can be used by supply chain leaders in maintenance, procurement, and logistics to lower equipment downtime, material inventory buffers, and material expedites.

Suggested activities and cooperation

When a risk is identified, AWS Supply Chain automatically assesses, ranks, and distributes several rebalancing options to give inventory managers and planners suggested courses of action. The sustainability impact, the distance between facilities, and the proportion of risk mitigated are used to rate the recommendation options. Additionally, supply chain managers can delve deeper to examine how each choice would affect other distribution hubs around the network. Additionally, AWS Supply Chain continuously learns from your choices to generate better suggestions over time.

AWS Supply Chain has built-in contextual collaboration features to assist you in reaching an agreement with your coworkers and carrying out rebalancing activities. Information regarding the risk and suggested solutions are exchanged when teams message and chat with one another. This speeds up problem-solving by lowering mistakes and delays brought on by inadequate communication.

Demand planning

In order to help prevent waste and excessive inventory expenditures, AWS Supply Chain Demand Planning produces more accurate demand projections, adapts to market situations, and enables demand planners to work across teams. AWS Supply Chain employs machine learning (ML) to evaluate real-time data (such open orders) and historical sales data, generate forecasts, and continuously modify models to increase accuracy in order to assist eliminate the manual labor and guesswork associated with demand planning. Additionally, AWS Supply Chain Demand Planning continuously learns from user inputs and shifting demand patterns to provide prediction updates in almost real-time, enabling businesses to make proactive adjustments to supply chain operations.

Supply planning

AWS Supply Chain Supply Planning anticipates and schedules the acquisition of components, raw materials, and final products. This capability takes into account economic aspects like holding and liquidation costs and builds on nearly 30 years of Amazon experience in creating and refining AI/ML supply planning models. Demand projections produced by AWS Supply Chain Demand Planning (or any other demand planning system) are among the extensive, standardized data from the AWS Supply Chain Data Lake that are used by AWS Supply Chain Supply Planning.

Your company can better adapt to changes in demand and supply interruptions, which lowers inventory costs and improves service levels. By dynamically calculating inventory targets and taking into account demand variability, actual vendor lead times, and ordering frequency, manufacturing customers can improve in-stock and order fill rates and create supply strategies for components and completed goods at several bill of materials levels.

N-Tier Visibility

AWS Supply Chain N-Tier Visibility extends visibility beyond your company to your external trading partners by integrating with Work Order Insights or Supply Planning. By enabling you to coordinate and confirm orders with suppliers, this visibility enhances the precision of planning and execution procedures. In a few simple actions, invite, onboard, and work together with your trading partners to get order commitments and finalize supply arrangements. Partners provide commitments and confirmations, which are entered into the supply chain data lake. Subsequently, this data can be utilized to detect shortages of materials or components, alter supply plans with fresh data, and offer more insightful information.

Sustainability

Sustainability experts may access the necessary documents and datasets from their supplier network more securely and effectively using AWS Supply Chain Sustainability, which employs the same underlying technology as N-Tier Visibility. Based on a single, auditable record of the data, these capabilities assist you in providing environmental and social governance (ESG) information.

AWS Supply Chain Analytics

Amazon Quicksight powers AWS Supply Chain Analytics, a reporting and analytics tool that offers both pre-made supply chain dashboards and the ability to create custom reports and analytics. With this functionality, you may utilize the AWS Supply Chain user interface to access your data in the Data Lake. You can create bespoke reports and dashboards with the inbuilt authoring tools, or you can utilize the pre-built dashboards as is or easily alter them to suit your needs. This function provides you with a centralized, adaptable, and expandable operational analytics console.

Amazon Q In the AWS Supply Chain

By evaluating the data in your AWS Supply Chain Data Lake, offering crucial operational and financial insights, and responding to pressing supply chain inquiries, Amazon Q in AWS Supply Chain is an interactive generative artificial intelligence assistant that helps you run your supply chain more effectively. Users spend less time looking for pertinent information, get solutions more quickly, and spend less time learning, deploying, configuring, or troubleshooting AWS Supply Chain.

Read more on Govindhtech.com

[tecnoconexx]

Desvendando o Mundo dos Dados: Data Warehouse, Data Lake e Além

Nos bastidores da revolução digital, os dados são o novo ouro. Para organizá-los e extrair valor, várias tecnologias e conceitos emergem como pilares essenciais. Vamos explorar alguns deles e entender como cada um contribui para a gestão inteligente de dados.

[anusha-g]

What are the components of Azure Data Lake Analytics?

Azure Data Lake Analytics consists of the following key components:

Job Service: This component is responsible for managing and executing jobs submitted by users. It schedules and allocates resources for job execution.

Catalog Service: The Catalog Service stores and manages metadata about data stored in Data Lake Storage Gen1 or Gen2. It provides a structured view of the data, including file names, directories, and schema information.

Resource Management: Resource Management handles the allocation and scaling of resources for job execution. It ensures efficient resource utilization while maintaining performance.

Execution Environment: This component provides the runtime environment for executing U-SQL jobs. It manages the distributed execution of queries across multiple nodes in the Azure Data Lake Analytics cluster.

Job Submission and Monitoring: Azure Data Lake Analytics provides tools and APIs for submitting and monitoring jobs. Users can submit jobs using the Azure portal, Azure CLI, or REST APIs. They can also monitor job status and performance metrics through these interfaces.

Integration with Other Azure Services: Azure Data Lake Analytics integrates with other Azure services such as Azure Data Lake Storage, Azure Blob Storage, Azure SQL Database, and Azure Data Factory. This integration allows users to ingest, process, and analyze data from various sources seamlessly.

These components work together to provide a scalable and efficient platform for processing big data workloads in the cloud.

[shristisahu]

Data Lake vs Data Warehouse: Crucial Contrasts Your Organization Needs to Grasp

Originally Published on: QuantzigData Lake vs Data Warehouse: Key differences your organization should know

Introduction: Data warehouses and data lakes are pivotal in managing vast datasets for analytics, each fulfilling distinct functions essential for organizational success. While a data lake serves as an extensive repository for raw, undefined data, a data warehouse is specifically designed to store filtered, structured data for predefined objectives.

Understanding the Distinction:

Data Lake: Holds raw data without a defined purpose.

Data Warehouse: Stores filtered, structured data for specific objectives. Their distinct purposes necessitate different optimization approaches and expertise.

Importance for Your Organization:

Reduce Data Architecture Costs: Understanding the difference between a data lake and a data warehouse can lead to significant cost savings in data architecture. Accurately identifying use cases for each platform enables more efficient resource allocation. Data warehouses are ideal for high-speed queries on structured data, making them cost-effective for business analytics. Meanwhile, data lakes accommodate unstructured data at a lower cost, making them suitable for storing vast amounts of raw data for future analysis. This helps prevent redundant infrastructure expenses and unnecessary investments in incompatible tools, ultimately reducing overall costs.

Faster Time to Market: Data warehouses excel in delivering rapid insights from structured data, enabling quicker responses to market trends and customer demands. Conversely, data lakes offer flexibility for raw and unstructured data, allowing swift onboarding of new data sources without prior structuring. This agility accelerates experimentation and innovation processes, enabling organizations to test new ideas and iterate products faster.

Improved Cross-Team Collaboration: Understanding the difference between a data warehouse and a data lake fosters collaboration among diverse teams, such as engineers, data analysts, and business stakeholders. Data warehouses provide a structured environment for standardized analytics, streamlining communication with consistent data models and query languages. In contrast, data lakes accommodate various data sources without immediate structuring, promoting collaboration by enabling diverse teams to access and analyze data collectively.

Conclusion: The distinction between a data lake and a data warehouse is crucial for optimizing data infrastructure to balance efficiency and potential. Developing accurate data warehouses and data lakes tailored to organizational requirements is essential for long-term growth and strategic decision-making.

Success Story: Data Synergy Unleashed: How Quantzig Transformed a Business with Successful Integration of Data Warehouse and Data Lake

Client Details: A leading global IT company

Challenges:

Fragmented and Duplicated Solutions

Separate Data Pipelines

High Manual Maintenance

Recurring Service Time-Outs

Solutions:

Implemented Data Lakehouse

Self-Healing Governance Systems

Data Mesh Architecture

Data Marketplace

Impact Delivered:

70% reduction in the development of new solutions

Reduced data architecture and maintenance costs by 50%

Increased platform utilization by 2X.

Unlock Your Data Potential with Quantzig - Contact Us Today!

[alex-merced-web-data]

🚀 **Maximizing Data Lake Query Performance: The Impact of Concurrency and Workload Management**

The efficiency of querying vast data lakes directly correlates with an organization's agility and decision-making speed. However, one critical aspect that often gets overlooked is how concurrency and workload management can significantly affect query performance.

**Concurrency** refers to multiple users or applications accessing the data lake simultaneously. High levels of concurrency can lead to resource contention, where queries compete for limited computational resources (CPU, memory, I/O bandwidth), leading to slower response times and degraded performance.

**Workload Management**, on the other hand, involves prioritizing and allocating resources to different tasks. Without effective workload management, critical queries can get stuck behind less important ones, or resources can be inequitively distributed, affecting the overall system efficiency. (The Dremio Lakehouse platforms has rich workload management features)

**So, what can we do to mitigate these challenges?**

1. **Implement Workload Management Solutions**: Use tools and features provided by your data lake or third-party solutions to prioritize queries based on importance, ensuring that critical analytics and reports run smoothly.

2. **Optimize Resource Allocation**: Dynamically adjust resource allocation based on current demand and query complexity. This can involve scaling resources up during peak times or reallocating resources from less critical tasks.

3. **Partition and Cluster Data Efficiently**: By organizing data in a way that minimizes the amount of data scanned and processed for each query, you can reduce the impact of concurrency issues.

4. **Monitor and Analyze Query Performance**: Regularly monitoring query performance can help identify bottlenecks caused by high concurrency or poor workload management, allowing for timely adjustments.

5. **Leverage Caching and Materialized Views**: Caching frequently accessed data or using materialized views can significantly reduce the load on the data lake (The Dremio Lakehouse platform offers reflections which makes this even easier and faster), improving performance for concurrent queries.

In conclusion, understanding and managing the impacts of concurrency and workload on query performance is crucial for maintaining a high-performing data lake environment. By adopting a strategic approach to resource management and query optimization, organizations can ensure that their data infrastructure remains robust, responsive, and ready to support data-driven decisions.

#DataLake #QueryPerformance #Concurrency #WorkloadManagement #DataStrategy #BigData

[innovaticsblog]

Optimize your data strategy by designing a data lake framework in AWS. Our guide provides expert advice on creating a scalable, efficient solution.

[ashratechnologiespvtltd]

Greetings from Ashra Technologies

we are hiring.....