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Big Data, Big Innovations

January 7, 2019

With a traditional data warehouse powering big data, it’s not unusual for data loads and complex queries to run for days, which hinders the analytical process. Plus, these data warehouse environments are often designed to analyze structured data only, and not valuable unstructured data generated from new external sources such as social media and mobile computing.

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Data protection has become an integral and essential part of any successful business. The need to provide a powerful, scalable, and simple disaster and operational recovery solution is at an all-time high.

White Paper on Privacy and Data Protection

whitePaper | March 30, 2022

Inching a step closer to framing a robust data protection regulation for India, the Joint Parliamentary Committee (“Committee”) submitted its report to the Parliament of India on the Personal Data Protection Bill 2019 (“2019 Bill”) on 16 December 20211 , after deliberatingfor almost two years. With that, the Committee also presented a revised bill, i.e., the Data Protection Bill 2021 (“2021 Bill”). Although presently there is no dedicated legislation in India addressing data privacy and data protection, on a sector-neutral basis, the Information Technology Act 2000 (“IT Act”) and the Information Technology (Reasonable security practices and procedures and sensitive personal data or information) Rules 2011 (“SPDI Rules”) regulate aspects of data privacy and protection in India today.

Introducing Apache Druid

whitePaper | January 31, 2020

Many companies have invested heavily in specialized enterprise data warehouses (EDW) and Extract, Transform and Load (ETL) technologies to analyze their operational data. But these technologies were never designed to be truly real-time.They were originally built for batch, and that original design limits how real-timeEDWs and ETL can become. They were also designed to support a focused group ofanalysts, not a larger group of employees spanning operational functions, or even partner and end customers.

A Modern Data Architecture

whitePaper | April 7, 2021

Assembling the perfect data stack is impossible. But for most data teams, the path to leveraging rapidly evolving tech and best-in-class tools is even more difficult when it’s impeded by the pitfalls of monolithic legacy applications.

Build a thriving business using data and analytics at scale

whitePaper | February 7, 2020

The problem is that growing volumes of data can lead to data paralysis, because no one knows quite where to begin or how to use it. Siloed information sources, no data management strategy, disconnected spreadsheets or analytical tools and poor data quality all work to compound the issues you’re facing.

Big Data, Big Innovations