Data Lakes
A data lake is a centralized storage repository that allows you to store all your structured and unstructured data at any scale. It enables you to retain data in its raw form without the need for upfront structuring and supports a wide range of analytics—including dashboards, visualizations, big data processing, real-time analytics, and machine learning—to drive more informed decision-making.
Data lakes provide the flexibility to accommodate diverse data types and sources, making them ideal for modern, data-driven organizations.
With the ability to separate storage from compute, they also offer scalable, cost-effective data management for evolving business needs.
What is a Data Lake?
-
Data Movement
-
Ingest any volume of data in real time from multiple sources
-
Store data in its raw format without requiring predefined structures or schema
-
Easily scale to accommodate growing data while reducing preparation time
-
-
Secure Storage & Data Cataloging
-
Store both structured (e.g., databases, business apps) and unstructured data (e.g., IoT devices, mobile apps, social media)
-
Use crawling, cataloging, and indexing to understand and manage your data
-
Implement robust security measures to protect your data assets
-
-
Analytics
-
Provide access to data for various roles—data scientists, developers, and analysts
-
Support a wide range of tools and frameworks, including Apache Hadoop, Spark, Presto, and commercial BI tools
-
Run analytics directly on the data lake without moving data to separate systems
-
-
Machine Learning
-
Enable insights from historical data and predictive modeling
-
Build and train ML models to forecast outcomes and recommend optimized actions
-
What are the capabilities of a Data Lake?
Learn More about
Data Lakes
The ability to gather and analyze large volumes of data from diverse sources in less time—and empower teams to collaborate using various tools—leads to faster, more informed decision-making. Data lakes enable this by providing a centralized platform for unified data access and analysis. Examples of the value data lakes bring include:
-
Enhanced Customer Insights
Combine data from CRM systems, social media, marketing platforms, and support tickets to better understand customer behavior. This helps identify the most valuable customer segments, uncover reasons for churn, and develop targeted promotions that improve loyalty.
-
Accelerated R&D and Innovation
Empower research teams to test hypotheses, validate assumptions, and evaluate results using diverse data sets. This could involve selecting optimal materials for product design, conducting genomic research for more effective treatments, or assessing customer preferences to guide product features and pricing.
-
Improved Operational Efficiency
Leverage real-time data from IoT devices to monitor and optimize manufacturing or business processes. With a data lake, machine-generated data can be easily stored and analyzed to reduce costs, enhance quality, and identify areas for performance improvement.