In the previous article, we talked about a data lake and its differences from a data warehouse. In this article, we will continue the data lake subject, looking at common use cases in which a data lake is a better option than a data warehouse.
A data lake is a large, scalable repository that keeps raw, unprocessed data in its original format, regardless of whether it is structured, semi-structured, or unstructured data. A data lake is designed to handle massive amounts of data; it supports a variety of analytics, including machine learning, big data processing, and real-time analytics. The ability to store any types and amounts of data allows companies to be more flexible and adapt to changing circumstances and business requirements.
Below we look at three scenarios in which a data lake serves as an ideal data storage solution.
Data integration during company mergers and acquisitions
A data lake is a perfect choice for mid to large-sized enterprises that are planning or have recently acquired smaller companies. The acquisition and subsequent integration of companies requires the transfer of large amounts of data between different places. Once your company acquires another company, accessing, transferring, and analyzing their data quickly and effectively becomes a key project.
This could be a lengthy and complicated process, especially if the data structure of the acquired company is different from your own or has unstructured data. This is often the case, as during mergers and acquisitions, data engineers integrate different data types and structures. A data lake can be a lifesaver, allowing quick data migration without having to worry about structure and ETL. Migrate the data into a data lake and start initial querying it right away.
The strategy is to set up a data lake first, migrate all the data, and then build a warehouse or semantic layer on top of that. The analytics team can later refine and model the data to enhance reporting.
Data Lake is cost-effective storage solution
With the development of cloud computing, storage costs have decreased, allowing companies to store large volumes of data. As seen in the previous example, when companies merge and need to integrate their data, a data lake serves as a cheaper storage solution than traditional data warehouses.
In 2020, the cloud computing market was over USD $370 billion, with projections indicating a rise to USD $832 billion by 2025. This trend shows a clear and continuous surge in cloud storage.
When it comes to comparing costs, storing the same volume of data in a data lake is typically cheaper than in a data warehouse. For example, Microsoft Azure Data Lake Storage offers significant cost savings using a pay-as-you-go pricing model. This can be a substantial cost saving over traditional data warehousing, especially when storing large amounts of data.
Prepare for AI advancements Using Data Lake
Data lakes are designed to support Machine Learning (ML) and Artificial Intelligence (AI) applications. Tools from platforms like Microsoft Azure help to effectively process data in a data lake and implement AI models.
As AI development gains momentum, companies should start preparing for its widespread adoption by using a data lake. A Gartner survey indicated that AI implementation grew 270% over four years, and the trend shows no signs of slowing down. In the near future, we can anticipate substantial advancements in natural language querying for datasets. Leading IT companies, like Microsoft, are preparing to use analytics chatbots to interact with big data storage.
Companies that have data lakes can direct new AI tools toward their data almost immediately. This capability will be a significant advantage for any company. Even though we are at the beginning stage of AI adoption, it is essential to be pro-active and avoid getting caught off guard when this groundbreaking technology becomes common everywhere.
The adoption of a data lake relies on your company’s specific needs and goals, considering data types, volume, and analytical requirements. The data lake is particularly advantageous in scenarios like mergers and acquisitions, cost-effective storage needs, and in anticipation of the growing AI trend.
Used correctly, a data lake is a transformative tool, enabling advanced analytics and providing necessary flexibility in a data-centric world. As we advance into the era of big data and AI, the capacity for quick and affordable storage and processing of diverse data forms will be vital. Prepare now and stay ready.