What is Data Lake?
Data lake is a place where all sort of data are stored or ingested in order to perform any kind of data analytics, business intelligence, machine learning, data warehousing etc.
Why should we use Data lake?
For the past few years, data have been growing exponentially and we are getting humongous data from different sources like mobile devices and extensively from IOT these days. Mostly those data are diverse and having open formats. Hence we use data lake as a central catalog for managing those increasingly diverse different format data like tables, images, videos and so on. Those data can be ingested into the data lake and can be managed and used for different purposes like stated earlier.
What are the different tools available for Data lake?
While we have some powerful tools in the market for data analytics like Power BI, Python, Knime, Tableau and so on, we do have some robust tools for the data lake as well mainly in the cloud. Few such offerings of the best data lake tools in the cloud are AWS Lake Formation and Azure Data Lake Storage.
What AWS has to offer for Data lake?
The simplicity in implementing or setting up of data lake makes AWS a super player in the cloud competition. AWS has S3 which is more robust and resilient that can store even years old data with a less expensive form. It includes many different tiers of storage that makes it more dynamic and use data in a more effective manner. Also as mentioned earlier, AWS offers the Lake Formation solution that very well integrates with different services like S3, Redshift, Athena, Kinesis, EMR and so on.
Conclusion
Data is everywhere nowadays and hence the necessity to manage the same effectively has become vital. It started with the traditional databases where we simply managed the tables and mainly focussed on the business transactions. Then came the data warehousing concept with which we ingested some important data from business for analysing and improving the businesses. In the modern days, as the technology transformed to a different level, the data processing for automation, machine learning, AI increases and so is the demand for data lake is increasing enormously.
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