Learn more about the differences and relationship between Big Data, Data Lake, and Big Data Analytics.
Because of advances in technology, the definition of Big Data has changed over the years. Yet, one thing that remains the same is that the amount of data is continuously growing at an extremely rapid rate. All data, in any form, that is used for gaining insight and generating value is considered Big Data.
Big Data Analytics is the practice of applying modern analytics software tools across data of all types, including unstructured, semi-structured, and structured data; as well as real-time/streaming and batch. The purpose of Big Data Analytics is to discover insights and irregularities, and to enhance the understanding of business performance and customer behavior. These analytics-driven insights can be used to shape business outcomes, improve competitive advantage, enhance financial decisions and develop more concise projections.
A Data Lake is scale-out storage for your Big Data. It allows for Big Data accessibility– via traditional and next-generation access methods– to enable in-place analytics. With a Data Lake, you can keep more data and keep it longer, making it essential for true Hadoop environments. An EMC Isilon Data Lake powered by Intel ® is the perfect way to get started.
The extensions to the Data Lake offer Big Data and analytic options for real-time and transactional use cases like Splunk, MongoDB and Cassandra.
© Intel, the Intel logo, the Intel Inside logo and Xeon are trademarks of Intel Corporation in the U.S. and / or other countries
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