What are the Key Capabilities of Data Lake Analytics

What are the Key Capabilities of Data Lake Analytics?

Posted on November 16, 2017

Data lake analytics is an on-demand service that simplifies big data analytics. The “Big Data” as the name suggests, is a colossal amount of data which can either be structured or unstructured. In order to analyze the big data, especially the unstructured one, you need superior expertise and advanced tools.

Businesses across the world use big data to gain valuable insights that can help them make informed business decisions, correctly comprehend the current market trends, and understand the expectations of the customers to gain an edge over their competitors.

Data lake analytics eliminates the need for deploying, configuring, and tuning the hardware while providing you with the flexibility to write various queries for transforming the data and extracting valuable insights. This analytics service can handle the jobs of any scale and you only need to pay for the running jobs. Indeed, it’s a highly time-efficient and cost-effective way of extracting resourceful information from the big data.

5 Key Capabilities of Data Lake Analytics

If you are wondering that what Azure data lake analytics can do, then, here are its key capabilities, which differentiate it from the other tools hailing from the similar category.

  1. Includes U-SQL

    The Azure data lake analytics includes the U-SQL, which is a query language that extensively extends simple and declarative nature of SQL with C#’ expressive power. Also, U-SQL has been built on the same distributed runtime which powers the “big data” systems installed at Microsoft.

  2. Faster Development and Smarter Optimization

    As it is deeply integrated with Visual Studio, one can use several familiar tools for running, debugging and tuning your code. The U-SQL job’s visualizations let you check how your code is running at scale and enable you to easily optimize the costs and identify performance bottlenecks.

  3. Compatible With All Types of Azure Data

    Data lake analytics has been optimized to be compatible with Azure data lake facilitating the highest level of parallelization, throughput, and performance for big data workloads. Data lake analytics is also compatible with Azure SQL Database and Azure Blob Storage.

  4. Cost Effectiveness

    Data lake analytics is highly cost effective and can easily be used on the big data workloads. The best part is that you only have to pay for exactly what you use. The payments are processed on per-job basis and you aren’t required to invest in any licenses, hardware, or any sort of service-specific support agreements. The system scales downs or scales up automatically when the job starts and completes, and this is why you will never have to pay for more than what you used.

  5. Dynamic Scaling

    The Data Lake Analytics has been specifically architected for the cloud scale and performance. It is capable of dynamically provisioning the resources and allows you to perform the analytics on the colossal data ranging in terabytes to even exabytes in size. After the completion of the job, the resources are wind down automatically.

Partner with Flatworld – A pioneer in Data Analytics Services

Flatworld Solutions is a pioneer in providing world-class data analytics services. With an overwhelming amount of data being available from various sources, it has become important for organizations to ensure that the data which they are going to use for developing business insights is correct and precise. We, at Flatworld Solutions, have a team of seasoned data analytics experts who assist our clients with valuable analysis of data and help them derive resourceful business insights from the data to gain an edge over their competitors. We also provide accurate and reliable big data analytics services. To know more about our data analytics services, you can contact us and post your queries/questions in the comment box provided below. Our executives will quickly get back to you with the best possible solutions.

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