Data Lake Implementation — Expected Stages and Key Considerations

For modern enterprises, efficient data management must be a key priority. Not only does it drive effective decision-making among business stakeholders, but it’s also critical for numerous processes including personalization, IoT data monitoring and asset performance management.

Often, however, traditional data storage methods prove to be pricey and come with the risk of vendor lock-in. For those reasons, many businesses are moving away from their traditional data warehouses and opting for data lakes instead.

A data lake is a storage system that holds substantial amounts of raw data in its original format. Storing a massive amount of data in this way costs significantly less than maintaining a data warehouse, which holds only processed and structured data. Raw data also grants greater flexibility and more uses than processed data. Using a data lake, then, empowers businesses to stay efficient and competitive in a data-driven world.

This article highlights what enterprises can anticipate when beginning their data lake implementation. In addition to explaining the typical journey of data lake development and maturity, we’ll also pose key questions that enterprises must address before and during the process.

Stages of Enterprise Data Lake Implementation

As with all other major technology overhauls, an enterprise should approach the data lake implementation in an agile manner. This means setting up a sort of minimum viable product (MVP) data lake that your teams can test in terms of data quality, storage, access and analytics processes. You can then add more complexity with each advancing stage.

Most companies go through four basic stages of data lake development and maturity.

Stage 1 — The Basic Data Lake

At this stage, the team begins to establish the foundational data storage capabilities. This includes making critical decisions regarding the use of legacy or cloud-based technology for the data lake and finalizing the security and governance practices that need to be baked into the infrastructure.

With a plan in place, the team builds a scalable, low-cost data lake, separate from the core IT systems. It is a small addition to the core technology stack, with minimal impact on existing infrastructure.

In terms of capability, the Stage 1 data lake can store raw data coming in from different enterprise sources and combine data from internal and external sources to provide enriched information.

Stage 2 — The Sandbox

The next stage opens the data lake to data scientists as a sandbox for conducting initial experiments. With data collection and acquisition managed, data scientists can concentrate on exploring innovative ways to use the raw data. They can bring in open-source or commercial analytics tools to create required test beds and design experimental analytics models tailored to various business needs.

Stage 3 — Complementing Data Warehouses

In the third stage of implementation, enterprises integrate the data lake alongside their existing data warehouses. Data warehouses prioritize intensive extraction from relational databases, while the data lake handles low-intensity extraction and stores cold or infrequently accessed data. This strategy prevents data warehouses from reaching storage limits, ensuring that even less critical datasets are preserved. The data lake also provides an avenue for deriving insights from this data or querying it for information not indexed by traditional databases.

Stage 4 — Driving Data Operations

In the final stage of maturity, the data lake becomes a central part of the enterprise data architecture, driving all data operations. At this point, it has taken over from other data stores and warehouses, becoming the only source for all data flowing through the enterprise. Also by this stage, the enterprise has implemented robust security and governance measures to effectively manage the data lake.

The data lake now enables the enterprise to

• Build complex data analytics programs that serve various business use cases

• Create dashboard interfaces that combine insights from the data lake with those from other applications or sources

• Deploy advanced analytics or machine learning algorithms, as the data lake manages compute-intensive tasks

Factors to Consider Before Implementing a Data Lake

Although the agile approach can effectively drive a data lake implementation, roadblocks might arise that could kill the project’s momentum. Typically these challenges come in the form of decisions about infrastructure and processes that need to be sorted out before launching the data lake implementation.

Pausing to address these questions midway through the project can cause delays and constraints, as you will need to factor in how these decisions affect work that is already completed. Taking the following steps before starting a data lake project can help you avoid such delays.

Pin Down the Use Cases

Most teams jump to technology considerations as their first point of discussion. However, defining a few impactful use cases for the data lake should take priority over deciding the technology involved. These defined use cases will help you highlight some immediate returns and business impacts of the data lake, which is crucial for maintaining support from those higher up the chain of command and for keeping project momentum.

Get the Storage Right

The primary objective of the data lake is to store the vast amount of enterprise data generated in their raw format. Most data lakes will have a core storage layer to hold raw or very lightly processed data. Additional processing layers are added on top of this core layer, to structure and process the raw data for consumption into myriad applications and BI dashboards.

You can build your data lake on legacy data storage solutions such as Hadoop or on cloud-based solutions such as those offered by AWS, Google and Microsoft. When making your decision, keep in mind that your data storage should:

• Ensure your data lake architecture can scale with your needs without running into unexpected capacity limits

• Be designed to support structured, semi-structured and unstructured data in a central repository

• Include a core layer that can ingest raw data, enabling diverse schema applications as needed at the point of consumption

• Ideally decouple the storage and computation functions to allow independent scaling

Handle Metadata

Since information in the data lake is stored in its raw format, it can be queried and used for various purposes by different applications. To enable this flexibility, usable metadata that captures both technical and business meanings must be stored alongside the data. The optimal solution is to establish a separate metadata layer to apply different schemas to relevant datasets.

Essential elements to consider while designing a metadata layer include

• Making metadata creation mandatory for all data being ingested into the data lake from all sources

• Automating metadata creation by extracting information from the source material, which is feasible in a cloud-based data lake

Ensure Security and Governance

You should integrate the security and governance of enterprise data into the design from the beginning, aligning it with the overall security and compliance practices of the enterprise. Key considerations include

• Ensure data encryption, for data in storage as well as in transit; most cloud-based solutions provide encryption by default for core and processed data storage layers;

• Implement network-level restrictions to block big chunks of inappropriate access paths;

• Create fine-grained access controls in tandem with the organization-wide authentication and authorization protocols; and

• Design a data lake architecture that enforces basic data governance rules regarding the compulsory addition of metadata, defined data completeness, accuracy, consistency requirements and other elements.

Addressing these questions in advance will ensure a smooth and steady pace for your data lake implementation. By carefully considering these factors, you can set a solid foundation for a successful project that meets your organization’s data management needs effectively.

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