What Is a Data Lake?
All organizations have diverse data sources and formats at varying structural levels. Data lakes are systems that store different data types in a centralized database.
Members of your institution can access and transform these data types later, giving them unfiltered data access and control and making lakes an essential infrastructural component.
What is a Data Lake in Simple Terms?
A data lake is a centralized storage base for large amounts of unrefined data. It stores raw data in varying phases, including structured, unstructured and semi-structured. Lakes keep data in its original format, giving users unfiltered, organic data for systematic analysis and decision-making.
Data Lake vs. Data Warehouse
Unlike data lakes, data warehouses process data before storing it. Most warehouses process raw data through an extract, transform and load (ETL) process.
The system retains data after a ‘schema on write’ transformation phase, storing the transformed data in structured formats like columns or tables. Using data lakes and warehouses together can give your organization valuable raw insights while giving you easy-to-consume formatting for excellent instant access.
Data Lake Uses and Benefits
The primary advantage of utilizing data lakes for your institution is collecting diverse data that opens your organization to intricate insights. Data lakes can store information in various formats like visuals or images, videos, text, code or log files.
You can integrate lake architecture into multiple sources like social media and digital platforms, internal information solutions, Internet of Things (IoT) technologies and more. The invaluable data your lake stores enables you to:
- Enhance collaboration and information-sharing.
- Minimize departmental silos.
- Evolve internal data access.
- Gain invaluable insights for decision-making.
- Identify core data patterns from unprocessed data analysis.
- Analyze performance and other data points for improvement areas.
- Design a comprehensive data ecosystem.
Data lakes are also versatile solutions that empower flexibility and scalability. They can collaborate with diverse systems and adapt to new and evolving technologies. This makes it easier to sustain data collection reliably and consistently, as your lake can grow with your organization. Their versatile integrations and scalability features also lend themselves to reduced data management costs, as you don’t have to adjust your storage systems regularly.
Exploring Data Lake Architecture and Best Practices
Data Lake Architecture
Key features and components of data lakes include:
- Extraction or ingestion.
- Secure data storage.
- Categorization or cataloging.
- Analytics integrations.
Data Lake Best Practices
To support efficient and quality use of your data lake architecture, you can implement these best practices:
- Establish specific aims for your system
- Create clear data governance frameworks, including policies, control and access management
- Plan a scalable data ecosystem
- Automate processes like quality assurance techniques using automated AI systems
- Prioritize system security and establish access control
- Consider data sources, data analysis platforms and other architecture you’ll use with your lake
- Track and maintain metadata and quality insights
- Ensure data quality controls and integrity through validation, cleansing and integration methodologies
- Reflect on your lake applications and set key performance indicators to enable you to identify areas for improvement
- Stay updated on evolving lake best practices and advanced tools you can integrate with data governance improvements
