Microsoft has prepared a New SQL Language for the Big Data Analytics

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THE NEW SQL LANGUAGE FOR BIG DATA

It is refreshing to see Microsoft shed the last bits of its not-invented-here mentality and accept new industry standards without conditions, the same way it did to Java two decades ago. It could be seen quite clearly with its support for Hadoop and Big Data. Microsoft, earlier this year announced plans for the Hadoop File System-compatible data store that’s called the Azure Data lake Store, which could run big analytics workloads. Data Lakes are a new term by the Big Data industry for massive stores of data, which are to be acted upon later. While some data analytics is intended for immediate or real-time processing, Data Lakes are more ‘set it aside and get to it later’.

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The Azure Data lake include all capabilities required to make it easy for data scientists, developers and analysts store data of any shape, speed and size and do all kinds of analytics and processing across languages and platforms. Furthermore, it removes complexities of storing and ingesting all data while making it faster to get it up and running with streaming, batch and interactive analytics. The Data Lake works with current IT investments for identifying, managing and securing for simplified management and governance of data. Also, it seamlessly integrates with operational stores and data warehouses to extend current data apps. Microsoft draws on the experience working with organization customers as well as running some of the biggest scale processing and analytics in the world for Microsoft businesses, including Xbox Live, Office 365, Azure, Bing, Windows and Skype. Azure Data Lake solves a lot of scalability and productivity challenges, which prevent one from maximizing the value of data assets with a service that is ready to meet current and future business requirements. It is the first cloud analytics service wherein one could easily develop and run massively parallel data transformation as well as processing programs in U-SQL.

KEY CAPABILITIES OF AZURE DATA LAKE

Some of the key capabilities of the Azure Data Lake are the following.

1. Made for Hadoop. The Data Lake store is an Apache Hadoop file system hat’s compatible with HDFS or the Hadoop Distributed File Systems and works with the ecosystem of Hadoop. The existing HDInisght apps or services that use the WebHDFS API could integrate easily with Data Lake Store. Also, it exposes a WebHDFS-compatible REST interface for apps. Data stored could be analyzed easily using the Hadoop analytics frameworks like Hive or MapReduce.
2. Performance-tuned for data analytics. The Data Lake store is built to run big scale analytics systems that nee4d massive throughput to analyze and query huge amounts of data. The data lake spreads parts of a file over several individual storage servers. This boosts the read throughput when reading the file in parallel to do data analytics.
3. Enterprise-ready: Highly secure and available. The system provides industry-standard reliability and availability. The data assets are durably stored by making redundant copies for guarding against any unexpected failures. Organizations could use Azure Data Lake in their solutions as an integral part of their existing data platform. Also, it gives enterprise-grade security for data stored.
4. Unlimited storage, petabyte files. It provides unlimited storage and suitable for storing different data for analytics. It does not impose any limits on file and account sizes, or the amount of data that could be stored in a data lake. Single files could vary from kilobyte to petabyte in size, which make it a good choice for storing any kind of data. Data is stored robustly by making several copies and no limit on the time duration for which data could be stored in the data lake.
5. All Data. The Lake store could store any data in their native form, as is, without a need for prior transformations. The data lake store does not require a schema to be defined before loading data, leaving it up to the one analytic framework to interpret data and define schema during analysis. Storing files of arbitrary formats and sizes make it possible for the Data Lake Store to handle semi-structured, structured and unstructured data. In essence, Azure Data Lake store containers for data files and folders. One could operate on the data stored using software development kits, Azure PowerShell and Azure Portal. As long as one puts data to the store using the interfaces and using the right containers, one could store any kind of data. The system does not do any special handling of data based on the kind of data that it stores.

APPS COMPATIBLITY WITH AZURE DATA LAKE STORE

The Azure Data Lake Store is compatible with most open source components in the Hadoop ecosystem. Also, it integrates nicely with other Azure services. This makes the Data Lake Store the perfect choice for any data storage requirements.

As the popularity of the data lake grows, so does the number of vendors joining the data lake waters, each bringing its own idea of what the data lake entails. While any data solution would have at its core a huge repository, there are some vendors that also roll in an analytics component or two, which is exactly what Microsoft plans to do. The data lake platform comprises three main services, including the Data lake Analytics, Data lake Store and Azure HDInsight. It provides the necessary repository to persist data influx, and Data Lake Analytics provide a mechanism for picking apart the data.

Dhrumit Shukla is Business Development Manager with TatvaSoft - a custom software development company. He writes about Technology Trends, experience working with B2B and B2C clients.