There are different ways and platforms when it comes to organizing and managing big data. Data lakes provide a complete and authoritative data store that can power data analytics, business intelligence, and machine learning.
Let us try to understand in simple terms what the Data Lake means in the technological world.
Imagine Data lake as a large container which is serving as a storage repository that can store large amounts of data in a variety of formats as unstructured, semi-structured and structured data. It is a place that can ingest every type of data in its native format with no fixed limits on account size or file.
Data lakes can process all data types like images, videos, audios and documents, which are critical for today’s Machen learning and advanced analytics use cases.
Data lakes are becoming increasingly important as people, especially in business and technology, want to better perform broad data exploration. This way, companies are able to reduce the amount of time finding and gathering data, enabling more time for analysis.
Bringing data together into a single place or most of it makes that much simpler. Being able to have the latest data, definitely helps businesses in performing advanced analytics and see the most updated information in comparison with their competitors.
The three V’s of today’s data pushes us toward acknowledging that there is no One-Size-Fits-all database for all data needs.
These V’s are, the Volume, the Velocity and Variety of today’s data.
The growth of the data in volume is enormous, with the spread of 5G technology this is going to get bigger and bigger due to its wide range of possibilities it offers.
The speed in which these changes are taking place with such a fast pace that according to many stats, it is said that 90% of data has been generated since 2016. This means as massive—and significant—as big data has already been in the past few years, it’s only going to get bigger as technology allows the world to become even more connected.
When it comes to variety, we know that in the early 2000s, streaming was limited to audio, while broadband internet was used mostly for web surfing, emailing and downloads.
Towards the end of the decade, with the spread of internet and the start of “smartphone era” the business priority shifted to streaming services for both audio and video, wide usage of social media, streaming video games platforms, and so on, all creating exponential consumption of data in different formats.
Below we can see the benefits of using Data lake as a solution for managing big data;
As we said these are only a few of the main benefits and there are other important reasons to get to know about.
There are two key differences between the two, and they are as follows.
Data lake tends to ingest data very quickly and prepare it later as people access it. With a data warehouse, on the other hand, you prepare the data very carefully upfront before you ever let it in the data warehouse.
In comparison to a hierarchical data warehouse, which stores data in files or folders and serves as a repository for structured and filtered data that has already been processed for a specific purpose, a data lake uses as flat architecture for storing data in its native, raw format, the purpose for which is not yet defined.
For the end, one extra benefit of having an immutable data ingestion layer storing all data ever ingested is highly valuable for audit, data discovery, reproducibility, and fixing any mistake in the data pipeline.
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