Data Lake Implementation: Best Practices and Key Considerations for Success
Centralization and limitless scalability across data storage, retrieval, and sorting are fundamental to enterprise-grade data lakes. A data lake is a one-stop repository that serves users’ data needs 24/7. Therefore, global organizations use it to store unfathomable amounts of structured, semi-structured, and unstructured data assets. This post describes the best practices and key considerations for a successful data lake implementation.
1| Explore How a Data Lake Will Create Value to Assign Correct Goals
Please do not succumb to the competitiveness pressures forcing you to approve a data lake implementation strategy only because your rival firms have done so. First, you must identify your company objectives. Without clearly defined data lake goals, organizations will struggle to actualize a reasonable return on data services.
2| Test Scalability and Upgradability for Tomorrow’s Big Data Applications
Developers, data engineers, and analysts collaborate to create a data lake and streamline big data storage workflows. They want to reduce the need for manual intervention when supervising vast data volumes.
The data lake should be flexible enough to accommodate different data sources. Similarly, it must adapt to the ever-changing governance requirements affecting modern businesses’ data practices. A data lake must also offer artificial intelligence (AI) add-ons for managing unstructured data.
3| Implement Robust Data Governance Frameworks and Technologies
An enterprise data lake must comply with ethical processing frameworks. How can it do so? Hiring a data governance officer (DGO) can empower its leadership to craft required policies. Additionally, establishing data validation protocols is essential, according to the data lake consulting services. Their significance only increases as tech-related data theft incidents, like ransom demand and corporate espionage, skyrocket.
Only authorized employees must have access to specific data sets. This requirement is vital to maintaining an adequate cybersecurity standard. Using governance rules, you can ensure regulatory compliance with Eurasian, Pan-American, African, and Australian consumer data protection criteria.
Key Considerations for Data Lake Implementation Success
Leaders must monitor the costs of storing and processing data in a data lake on a trial basis. They can conduct market studies and rate analyses to estimate IT costs. These practices will inform companies about potential cost-reduction ideas.
For example, cloud computing providers offer preconfigured data lake service packages. They will likely employ a usage-linked pricing strategy. Optimizing your data strategies to consume fewer resources will make data lake implementation economical.
At the same time, you want to train employees, suppliers, and fellow leaders on using the data lake and related tools. Otherwise, a lack of skill will cause them to waste time and effort instead of benefitting from data lake to boost the reliability of analytics.
Finally, stakeholders must quickly revise the data lake architecture when business needs evolve due to mergers, expansions, or regulatory pressures. For instance, your data lake implementation team must incorporate new data encryption technologies. Furthermore, you want to determine a balanced multi-cloud integration outlook.
The Bottom Line
Enterprises expect competitive advantages from a proper data lake integration. However, they must encourage in-house data professionals to ensure an equilibrium between scalability and security. Best data lake practices and key considerations like access control, thoughtfully specified implementation objectives, and periodic infrastructure upgrades can help achieve that.
The post Data Lake Implementation: Best Practices and Key Considerations for Success appeared first on Datafloq.
