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Why You Need a Data Lake for Big Data
Big data is quickly becoming every business’ best resource. In its 2018 Cloud Markets and Trends Report, Wikibon predicted a 17 percent compound annual growth rate for big data software over the next 10 years. Using the data and insights captured with big data solutions, companies are finding new ways to improve current business operations, uncover insights, and drive unexpected efficiencies.
Increasingly, the answer is to gather the data within a central repository: a data lake.
With data lakes, you can use archived data from any source for data-driven initiatives. This enables you to operationalize machine learning and process streaming data, ultimately bringing structure and access to big data.
Solutions for a Competitive Advantage
Transform Data into Insights
With Oracle's big data solutions, massive incoming data can be processed, stored, and utilized as the foundation for new business strategies benefiting:
- Business analysts: New insights for data-driven decisions
- Data scientists: Faster, easier modeling thanks to machine learning
- IT: Smarter data management to balance resources using a data lake
Fast, Smart, and Scalable Data Management
With data lakes, data can be centrally stored and processed for optimal usage:
- Consolidate data needed for analytics in a data lake for data management and processing
- Scalably execute machine learning and deliver results to downstream business applications
- Easily scale data lake storage and compute independently to meet workload requirements
- Deploy on-premises or in the cloud
Discover Data Lakes in Action
Solution patterns provide working top-level design that puts system components into place without getting bogged down in details. How should you structure your data lake? It depends on use case parameters such as:
- Existing hardware design
- Level of necessary transformation
- Extent of analytics usage
- Streaming data requirements
Use Data Labs with Data Lakes
Data lakes offer a resource-friendly way of storing and accessing data, but what happens when you need to dig deeper? A data lab is a separate environment where smaller data extracts can be manipulated, explored, and analyzed. A data lab can be used to:
- Optimize IT resources by working in a lab environment on a smaller, controlled scale
- Allow for experimentation to generate new insights and strategies
- Safely manipulate data without impacting the data lake
HR, Finance, Supply Chain, and More
Regardless of your line of business, big data can transform the way you analyze results and make decisions:
- HR: Evaluate retention, hiring, and employee satisfaction with real numbers
- Supply chain: Optimize inventory and manufacturing for efficient planning
- Finance: Utilize predictive modeling and analysis to stay ahead of markets
- Marketing: Get in-depth insight into how marketing plans are working—and why
Go Deeper with Oracle’s Big Data Products
Data Integration
Big data comes from a number of sources. Weaving all of that data into a comprehensive and manageable fabric is a process in itself. The result is a unified view of data, ready to be processed and analyzed.
Big Data Management
Managing big data sources, processes, and output is a tricky balancing act, especially when compliance and other factors come into play. Big data management handles this dynamic scenario for a streamlined and effective process.
Data Science
Through data science, numbers aren’t just numbers anymore. They tell a story about your organization’s past, present, and future. Data science is all about discovering that story.
Business Analytics
What business secrets are buried deep within volumes of data? Analytics extracts value from data through analysis and projection, unlocking the door to new strategic insights and possibilities.