A Data lake serves as a centralized repository for structured and unstructured data to deliver value within businesses. It allows data consumers to procure data from well-defined sources to support multiple use cases.
As per a recent report by Research and Markets, by 2026, the data lakes market is anticipated to reach a valuation of US$ 18.67 billion. To create a successful storage and management system to achieve credible data lakes, here are the best approaches companies need to follow.
Data lake ingestion allows the collection and absorption of data into object storage. Data ingestion is more straightforward because data lakes enable you to store semi-structured data in the native format in the architecture.
Therefore, complexities around data ingestion should be thought of early on. If the data is not stored efficiently, it might be challenging to access later. At the same time, proper data ingestion can help businesses resolve operational challenges like storage optimization for analytical performance with streamlined processing of updated event data.
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The critical reason businesses adopt a data lake is to store vast amounts of unstructured data with relatively low financial investment, saving long man hours. Keeping copies of the raw historical data can be utilized to assess past issues like error recovery, tracking data lineage, or exploratory analysis.
Data duplication this complex can be cumbersome, however, with modern managed infrastructure, storing data is inexpensive with no clusters to resize.
Most companies operating in a multi-cloud infrastructure need to make sure that the data lakes run on other platforms. Therefore, businesses need to check whether that the data infrastructure is capable enough to perform effectively in a multi-cloud infrastructure.
This can be achieved by opting for a flexible strategy allowing companies to maintain agility with changes in technology choices. A data vault methodology allows businesses to continuously onboard new types of data with a sound approach.
Additionally, a well-crafted data governance strategy is a fundamental practice for any big data project that ensures consistency in common processes and responsibilities. CISOs restrict storing the data in an unstructured repository. These bring challenges in setting specific row, column, or table-based permissions in a database.
Such challenges can be resolved with numerous governance tools that ensure businesses have control over the accessibility of the data.
Storing data in columnar storage makes data easy to read. Therefore, businesses need a robust plan to store the data to utilize it for analytics in a format such as Apache Parquet or ORC. At the same time, these file formats are open-source rather than proprietary, allowing users to read using multiple analytic services.
Additionally, data streams and logs produce thousands of small ‘event’ files. Hence, businesses need to apply “compaction” directly that merges these small files without impacting the performance.
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These are the best approaches to be practiced when establishing a data lake to ensure it serves the businesses with credibility to store, architect, and catalog the data. It will also allow the utilization of event sourcing to ensure data traceability and consistency.
Additionally, it allows data lake layering to resonate with the user’s skill granting access to the data utilizing well-known tools.
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Apoorva Kasam is a Global News Correspondent with OnDot Media. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in Blockchain, data governance, and supply chain management. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.
