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How to Architect a Successful Data Lake

A data lake helps businesses in solving a lot of business problems. By unlocking true value of data lake, Amazon scores 37 million clicks in just one day and clocks 426 sales in a second on an average. The benefits are immense, and experts say that we haven’t seen the real potential at all. As a strategic asset, a data lake can foster new ways of working, improve customer engagement & service, remove inefficiencies, and help in developing a minimum viable product. However, it requires a transformative vision to create a successful data lake that delivers overarching business benefits.

Here are six elements that business leaders must put together while architecting a stellar data lake that supports business growth and performance:

Strategy for Data Lake: Understanding the data environment &operational aspects like data formats, data ingestion techniques, data preparation, workflow and governance will help organizations in identifying the potential roadblocks. Therefore, the corporate strategy for a data lake must consider that how the reference architectures would deliver big data benefits and cost savings.

Data Integration Framework: To deal with large variety, velocity, and volume of data organizations will require a modern data integration approach. Organizations fail to seamlessly move the data into analytic pipeline and leverage the data without a robust data integration framework.A modern data integration tool for preparing data lake needs to be adaptable for diverse integration needs. More importantly, it should automate the process of cleaning, transforming, blending, visualizing, and analyzing data with any further technology investments.

Storage and Data Formats: Unlike traditional warehousing, a data lake needs to support a slew of storage and data formats. It should support arbitrary data formats like CSV, RCFile, Parquet, etc. to smoothly process data. A dedicated lake should focus on improving bandwidth utilization and supporting specific data query requirements.

Today’s data lake requires flexible tooling and pre-builtconnectorsto synchronize different data sources. Another pressing need is enabling metadata ingestion on data ingestion in a lake. This feature helps in confronting the challenges of micro-batches or real-time sensitivities.

Tools for Data Discovery: While dumping data into lake, teams create a lot of unstructured data. To query this data and pull out useful information, organizations will require necessary business intelligence tools. To make the most out of a data lake, entities must have advanced triggers to accurately investigate, clean, and pull raw data from different data sources.

Plan for Scheduling and Workflow: Data orchestration has become the new mandate in a data lake. A data lake needs to enable users in quickly developing responses and scheduling jobs for external triggers. It should provide a unified approach to orchestrate and schedule work in simple and straightforward ways. A centralized interface also ensures quick access for large group of business users.

Controls for Data Encryption: Cyber security threats have registered a huge spike in volume and variety. Therefore, advanced threat intelligence measures should be incorporated to strengthen security arrangements in a data lake. Smarter targeting and filtering options, selected authorization gateways, and demarcated data access can standardize security across the data lake of an organization.

A data lake can make a huge difference and dramatically improve business operations of an organization. However, harnessing a data lake without hassles requires a farsighted vision and strategy. Know how Adeptia can help you harness enterprise data lake to improve business operations.