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How To Leverage Data Lakes For Your Organization

Using Data Lakes To Generate New Insights From Data and Build Data Capabilities

As data lakes have increased in popularity over the past five years, cloud providers started offering data lake capabilities to make it easier to ingest, store, and centralize data across an organization. Most organizations benefit from data lakes as data is often stored in dozens to hundreds of disparate data environments, and the data can be represented in various formats, including structured and unstructured data. Data lakes enable organizations to store their data in a centralized, virtual data platform. Centralization provides many benefits, including predictive analytics, Artificial Intelligence (AI) capabilities, data storage across multiple data types (including streaming data and images/videos), data discovery and cognitive search, and granular data security controls.

What Is A Data Lake?

A data lake is a virtual, centralized repository that stores data from across an organization, regardless of data format, structure, or type. A data lake sends and receives data from any database, data warehouse, or API. Its virtual environment permits organizations to move towards data centralization for analytical purposes without decommissioning existing databases. This enables organizations with existing data systems to leverage the benefits of a data lake without deactivating existing or legacy systems. Once the data lake ingests and centralizes the data, data scientists and AI practitioners can build and deploy powerful AI analytics and solutions. These solutions will generate new insights, patterns, and relationships among all the integrated data.

Data Lakes Power Artificial Intelligence

As disciplines within AI continue to advance, the possibilities for both federal agencies and commercial organizations are endless. Perhaps most exciting are the continuously evolving fields of machine learning (ML) and deep learning, empowering companies to generate new insights from unstructured data. The problem that agencies face when they feel ready for AI, ML, and deep learning is that their data isn’t in a single environment where AI algorithms can easily access it. Data lakes power AI capabilities for this reason. Without data centralization, newly uncovered relationships, patterns, and correlations can’t be deduced as the AI algorithms will only have a smaller subset of data to work with. To build an AI model with a reasonable accuracy rate, high volumes of data are required, which can be stored within a data lake.

What Is A Data Lake Not?

Data lakes are not data warehouses. A data warehouse requires data to be pre-categorized and tagged before storage. Data lakes are flexible and can ingest and store data in its as-is format. With that said, it’s important to note that Extract, Transform, and Load (ETL) operations will need to occur to get data sets prepared for data analytics, ML, or AI models. AI and ML require data to be prepared in specific formats that predictive algorithms will understand, so it’s important to consider ETL when building analytical models. This is the case regardless if you’re using data stored in a data lake, data warehouse, or SQL environment.

Examples Of Capabilities That Can Be Built Using Data Lakes

Create A Google-like Search Engine Within The Data Lake

Following data centralization, agencies can index their data and create a search engine that returns relevant search results quickly while providing a user-friendly search experience.

Protect Data At The Column Or Cell Level

Data lakes enforce roles and access policies for every unique data set, including protecting data within an individual table column. This tactic enables granular data protections across various types of data. Additionally, it ensures the same security policies are carried over from the original data source to the data lake.

Implement Data Governance And Provenance

Knowing how data is accessed and disseminated across an organization is critical to enforce proper governance of the data. With capabilities like Cloudera Data Flow that integrate smoothly into Hadoop-based data lakes, an agency can visually track key data sets and see who’s accessing data, how, and what they’re doing with the data. This provides complete operational oversight into the data lifecycle and offers provenance once new data sets are ingested.

Want To Learn More?

Data lakes can empower your organization to execute new types of analytics to make faster, more informed decisions. Our AI experts at eGlobalTech implement data lakes for on-premise systems and cloud providers. This guidance enables our clients to securely store data while harnessing more data in less time. Our Senior Director of Technology Strategy and Head of EGT Labs, Jesus Jackson, will present at the O’Reilly Software Architecture Conference on data lake implementation and data lake use cases.

Have questions? Contact us at egtlabs@eglobaltech to find out how EGlobalTech can deploy data lakes to support your organization.


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