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Big Data: Managing Both Structured and UnStructured Data in Data Lakes
Big Data and analytics continued to gain momentum throughout 2017 and continue to grow now into 2018. But these big data technologies are growing at the expense of more traditional ones.
“As the volume, velocity and variety of data being generated continues to grow, and the requirements to manage and analyze this data continue to grow at a furious pace as well, the traditional data warehouse is increasingly struggling with managing this data and analysis. While in-memory databases have helped alleviate the problem to some extent by providing better performance, data analytics workloads continue to be more and more compute-bound,” said Nima Negahban, Kinetica CTO and Co-founder.
Large data repositories are at the heart of Big Data implementations. The use of Data Lakes is a technique increasingly used for managing the data used to fuel Big Data. A data lake is a storage repository that holds large amounts of data in its original raw native format. Data could be any of structured, semi-structured or unstructured. No data structures need to be defined until the data is actually needed A recent report by Syncsort reported on trends for Data Lakes expected to be seen this year:
- Data Lakes will be increasingly be deployed in the cloud .
- Data Lakes will include pools of data integrated from legacy systems.
- Top challenges for Data Lakes are ensuring quality and meeting regulatory compliances
- Keeping Data Lake information up-to-date becomes more important as real-time and predictive analytics gains in popularity
- Organizations will continue to make investments in Big Data and repositories like Data Lakes.
