3 Data Lake Solution Patterns for Big Data Use Cases

The way we captured the design was in what was called a working drawing. In those days it was neatly hand sketched showing shapes and dimensions from different perspectives and it provided enough information to cut and assemble the wood project. The big data solutions we work with today are much more complex and built with layers of technology and collections of services, but we still need something like working drawings to see how the pieces fit together.

These are the patterns:

Data Science Lab
ETL Offload for Data Warehouse
Big Data Advanced Analytics
Streaming Analytics

Data Science Lab Solution Pattern

Let’s start with the Data Science Lab use case. We call it a lab because it’s a place for discovery and experimentation using the tools of data science. Data Science Labs are important for working with new data, for working with existing data in new ways, and for combining data from different sources that are in different formats. The lab is the place to try out machine learning and determine the value in data.

The data lake object store can be populated by the data scientist using an Open Stack Swift client or the Oracle Software Appliance. If automated bulk upload of data is required, Oracle has data integration capabilities for any need that is described in other solution patterns. The object storage used by the lab could be dedicated to the lab or it can be shared with other services, depending on your data governance practices.

ETL Offload for Data Warehouse

Data warehouses are an important tool for enterprises to manage their most important business data as a source for business intelligence. Data warehouses, being built on relational databases, are highly structured. Data therefore must often be transformed into the desired structure before it is loaded into the data warehouse.

This transformation processing in some cases can become a significant load on the data warehouse driving up the cost of operation. Depending on the level of transformation needed, offloading that transformation processing to other platforms can both reduce the operational costs and free up data warehouse resources to focus on its primary role of serving data

Big Data Advanced Analytics

Advanced analytics is one of the most common use cases for a data lake to operationalize the analysis of data using machine learning, geospatial, and/or graph analytics techniques. Big data advanced analytics extends the Data Science Lab pattern with enterprise grade data integration.

Also, whereas a lab may use a smaller number of processors and storage, the advanced analytics pattern supports a system scaled-up to the demands of the workload.

Results are made available to Oracle Analytics Cloud for visualization and consumption by business users and analysts. Results like machine learning predictions can also be delivered to other business applications to drive innovative services and applications.

From data scientists and analysts, who work closely with company data each day, to business leaders exploring new ways to improve the way they work, Oracle has a set of rich integrated solutions for everybody in your organization.

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