Basic of Data Lake
“If your data lake is not clean, it is a data swamp, and you cannot swim in a data swamp, can you?”
― Rupa Mahanti, Data Humour
Now days most of people talk about data lake, what it is and how this amazing tool help us for building the simple software using complex data
What is it?
A data lake is a centralized repository designed to store, process, and secure large amounts of structured, semistructured, and unstructured data. It can store data in its native format and process any variety of it, ignoring size limits.
A data lake provides a scalable and secure platform that allows enterprises to: ingest any data from any system at any speed—even if the data comes from on-premises, cloud, or edge-computing systems; store any type or volume of data in full fidelity; process data in real time or batch mode; and analyze data using SQL, Python, R, or any other language, third-party data, or analytics application.
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Data lake vs data warehouse
Data lake vs. data warehouse: A data lake is also defined by what it isn’t. It’s not just storage, and it’s not the same as a data warehouse.
While data lakes and data warehouses all store data in some capacity, each is optimized for different uses. Consider them complementary rather than competing tools, and companies might need both. As a point of comparison, data warehouses are often ideal for the kind of repeatable reporting and analysis that’s common in business practices, such as monthly sales reports, tracking of sales per region, or website traffic.
Do you need a data lake?
When determining if your company needs a data lake, keep in mind the types of data you’re working with, what you want to do with the data, the complexity of your data acquisition process, and your strategy for data management and governance, as well as the tools and skill sets that exist in your organization.
Companies today are also starting to look at the value of data lakes through a different lens—a data lake isn’t only about storing full-fidelity data. It’s also about users gaining a deeper understanding of business situations because they have more context than ever before, allowing them to accelerate analytics experiments.
Developed primarily to handle large volumes of big data, companies can typically move raw data via batch and/or stream into a data lake without transforming it. Enterprises rely on data lakes in key ways to help:
