Accelerating Essential Data Initiatives with Data Lakes
Why do businesses prefer data lake over data warehouse? Why is there so much sudden traction? Most probably due to the flexible data lake architecture, detailed analytics, and data science. But, saying this is not enough to understand the context behind data lake, and why there’s a sudden rise in the usage of AWS data lake and Azure data lake.
This blog provides information on what is a data lake, its history, and its traction. A brief section on data warehouse vs data lake is also listed for you to understand the differences between both. Moreover, we’ve also covered why businesses are shifting to data lakes over data warehouses.
What is a Data Lake?
A data lake is kind of a centralized library or a repository that is primarily used to store and handle structured, unstructured, and semi-structured data in its raw and native format. Such storages use a uniform data lake architecture storage to preserve the data. Moreover, there’s no size limit for a data lake, it can process any form of data in its raw format.
A data lake for business intelligence purposes allows enterprises to handle the data no matter if it comes from an edge-computing system or a cloud. Furthermore, a data lake provides the privilege to process the data batches or streams in its fidelity. It also offers enhanced analytics features those enterprises can use in any language format.
History and Evolution of Data Lakes
Initially, data scientists were analyzing, and mining huge chunks of data named “Big Data”. Before that, large amounts of data were still used but without any official name. The data was then named Big Data by Roger Magoulas in 2005 when he used traditional SQL tools to research and manage the data and it was sort of impossible to do at that time. Hadoop later in 2008 came up with a search engine that eased the processing and locating of raw data on a big scale.
Data lakes initially started on Hadoop, but now due to continuous evolution are migrating to multiple cloud platforms due to their significant data management features and the concept of utilizing the unstructured data into meaningful information.
After this evolution, Microsoft came up with its data lake named Azure Data Lake, and there’s one by Amazon too, named AWS Data Lake.
Why Businesses Prefer Data Lakes – The Benefits
With the rise in the evolution of big data into the usage of data lakes, businesses have started to prefer data lakes alongside data warehouses.
One of the major reasons businesses prefer data lake is due it its non-schema feature. Schema is a restriction to store data in a particular format. In data analytics applications, a schema is a useless restriction since the major goal is to analyze the data as it is without having to disturb its structure. This feature to decouple the schema is excellent for data scientists’ machine learning applications.
Data lakes allow for better expandability and scalability of the business. When you consider business scalability through a data warehouse, you’ll have to encounter additional costs like storage costs, more query time, etc. However, with a data lake, you can enhance the decision-making qualities, and better decisions often lead to enhanced scalability. Moreover, with a data lake, expandability is much easier and inexpensive.
One of the data lake’s features is the availability of large amounts of coherent data with advanced deep learning and machine learning analytics. These features enable you to get real-time analytics and insights into the streaming data.
Not just because of its advanced features, businesses prefer to use data lake because it stores data in native raw format i.e., storing the data as it is in an unstructured, semi-structured, and structured format. Thus, you can simply store the data without modeling it at the time of storage ingestion. However, you can always adjust the data at the time of its usage for detailed results.
Data Warehouse vs. Data Lake: What’s the Difference?
Although, all businesses require a data warehouse and a data lake for business intelligence. However, these two have completely different features but both are necessary for each other's existence and sort of complement each other.
A data warehouse is a database that is used to analyze the relational data stream coming from various business applications and data systems. Unlike a data lake, you need to define a proper schema (schema-on-read), strategy, and a working structure for a data warehouse in advance so you can get fast queries. In a data warehouse, the data is manually adjusted, purified, and then transformed into a kind of data that end users can trust for their applications. Data warehouses are generally used for analytics and reporting that is leveraged through highly curated data. A data warehouse provides fast results on queries, but it costs more storage to leverage such features.
Unlike a data warehouse, a data lake stores both relational and non-relational data, making it possible to store structured, non-structured, and semi-structured data inside the data lake repository. For a data lake, you do not need to define a schema or working structure in advance (schema-on-write). This feature enables you to store the data 'as is’ without molding or disrupting it for future needs. Unlike a data warehouse, you can use a data lake for business intelligence, and real-time big data analysis to obtain valuable insights, particularly for data science and machine learning algorithms. Moreover, a data lake provides fast query results without having to expand the storage.
Importance of a Data Lake – Final Thoughts on its Positive Impact on Businesses
In short, we can say that a data lake allows you to store raw data and is a centralized repository that allows you to easily democratize the data in its native format.
If your business lags and takes time longer than expected to gain valuable insights into the data that flows, a data lake is a great option for you. It will allow you to preserve your data in its raw form that you can explore later when needed. Moreover, a data lake speeds up the process of data handling which is a challenging issue in today’s big data world.
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