Data Lakes
and Data Lake Rescue

Optimizing Data Collection and Centralization
With Structured Data Lakes

Are Your Analytics Drowning in Data Lakes?

The foundational, crucial requirement for supporting analytical solutions is a rich, reliable source of data.  Understandably, many data-related initiatives begin with the comprehensive collection and centralization of data. There are many collection strategies that are currently in use, including Data Lakes.

Treacherous Waters

Data Lakes are general repositories in which data is stored in its natural format.  Data objects within the “lake” may be text files, database tables, blobs, etc.  Data may be structured (e.g. tabular, XML, etc.) or unstructured (e.g. images, PDF’s, emails, texts, etc.).  The goal of the Data Lake is to offer a single storage location for all enterprise data to support analytics and data visualization.  Often Data Lakes utilize non-database, “big data” solutions (e.g. Hadoop) to accommodate and ingest unstructured data at incredible speeds.

On its face, the construction of a Data Lake seems like an intuitive and practical first step.  However, many Data Lakes become dumping grounds and data graveyards, in which undocumented data is recklessly deposited with the hope of supporting some undefined, future use. The typical Data Lakes suffers from a variety of defects:

  • Limited concern for the validity, completeness, and understanding of the data being collected
  • Insignificant regard for the compliant and responsible treatment of personally identifiable information (e.g. customer contact details, addresses, tax identification numbers, etc.; “PII”)
  • Insufficient data mastering and history accumulation
  • Lackluster performance of Big Data query tools often require data lake content to be (redundantly) re-instantiated in a structured environment (i.e. database) to be truly usable

The Structured Data Lake

Are you considering the creation of a data lake? 

Perhaps you have a data lake but aren’t seeing the broad organizational usage you expected.

With decades of experience delivering enterprise data integration solutions, the Lightwell team can guide you through the best practices for responsibly collecting, documenting, and assessing your organization’s structured data. 

The Lightwell Structured Data Lake is a rapid, durable first step towards high-performance, self-service analytics. Our solution features:

  • Prescribed mastering techniques for the 30+ types of content configurations your data ingestion might encounter
  • Simplified date chaining techniques to optimize daily history tracking of critical data performance
  • Sequestration techniques for personally identifiable information (PII) to support compliance with emerging customer privacy legislation
  • Automated profiling and metadata collection tools to accelerate intelligent data ingestion
  • Metadata resources to assist users with the navigation and utilization of data lake assets
  • Comprehensive data quality architecture

Structured Data Lakes vs. Typical Data Lakes

Similarities Improvements
  • Data is ingested in its natural form with little or no transformation
  • All data necessary to prepare a structured analysis is co-located within the Lake (rather than just selected structures or attributes)
  • Rapid implementation
  • Flexible accommodation of new and/or changed data structures
  • Pronounced emphasis on metadata
    (structure-level, attribute-level, domain-level, applicability conditions, etc.)
  • Responsible tracking and accumulation of historical performance
  • Responsible data mastering techniques
  • Structured data storage for optimized consumption by a broader collection of query tools

Rescue Your Data Lake

Are you ready for clear waters? Get in touch with us and let’s review how the Lightwell Structured Data Lake can improve your data quality and analytics.

Contact us today or call us at +1 (614) 310-2700, and we’ll connect you with one of our experts.