If you work in the tech industry, you might have heard the term data lake a lot whenever we talk about big data or storing vast amounts of data. While most people often confuse data lakes with the likes of data warehouses, the two terms aren't quite synonymous. While the former refers to the storage place where all the raw and unorganized data is placed, the latter is a repository for relatively structured and nicely organized data.
So, why are data lakes so popular in big data, and how can organizations use them to store vast information? Well, we will see all about it in today's article. So, make sure you give it a thorough read. Let’s get started without any further ado!
A data lake is a storage space where huge volumes of raw or processed data can be stored. Data lakes usually have data streams connected to them where the data is ingested in the form of huge batches or streams. It might or might not be synced in real-time, but it's updated frequently, depending upon the use case.
From the definition, it's clear that the purpose of data lakes is closely associated with data analysis since we're talking tons of data. However, if you have some knowledge of data analytics, you would know that raw and unstructured data, especially in such large volumes, is almost useless. So, how are data lakes used? Well, let's see the complete lifecycle of a data lake.
In this section, we will go through an abstract view of the different processes that the data goes through in a data lake to see how data lakes operate. Let's split the process down into the major steps:
This is the first step and a key component of a data lake. This is where the data gets into the lake. The data can be directed into a data lake manually, but mostly, the lakes are connected to data sources that dump the data into the lake on a regular basis.
The ingestion part is where the data is actually received and stored inside the data lake. Since the lakes are mostly connected to data sources that continuously provide data, the data ingestion is mostly done in the form of batches or streams.
ince data is fed in from multiple sources, it has to be blended in a specific way that matches the other data stored or according to some pre-set rules in order to ensure speedy data retrieval and insights. You can think of the blending process just like you get into a new team and blend into them once to adopt all their norms and stuff. This is important to keep the data from swamping.
Once the data is blended, the transformation is applied to convert the data to a particular structure or format. This step can also include any kind of data analysis if required. However, that's entirely dependent upon the use case you're dealing with. In some cases, this step might be absent altogether as well. Some popular tools used here are Spark, Hadoop, Hive, etc.
Once the transformation is done, the data is ready to be published wherever required. The publishing may include any kind of manual query to retrieve the data, or it might be done using some particular data publishing pipelines. However, this is strictly within the organization and not public.
The last step is where the data is actually distributed where it needs to be used. Mostly, the data distribution is done in the form of actionable insights or some important patterns/trends uncovered from the data. This can be done on several platforms where the findings are even accessible by the general public.
Data lakes are pretty essential when it comes to the big data industry. When we're talking about storing a huge volume of information centrally, they're the key players, even if it's present in raw form. The data retrieval is not only quick, but it's also quite scalable.
Throughout the article, we have seen the stages that the data passes through when being stored in a data lake and how the data lake functions on an abstract scale.