A data lake is a data storage repository that stores data in full—the data files are kept in their native format—until needed for analysis. Data lake integration is the process to move, prepare, and load data for analysis in a data warehouse. One benefit of a data lake is that it can cost-effectively hold vast amounts of raw data, including structured, semi-structured, and unstructured data until it’s ready to be used.
Why is Data Lake Integration Important?
Data lakes are very useful as storage repositories. Integration technology makes the stored data in a data lake helpful to the business by creating an automated path to an analytics system. The data lake provides a central location to collect data of all types that can be used for analysis when needed. A data lake is different from a data warehouse, which is ideally suited to analyze structured data that’s stored internally. With the right analytic database technology, queries can be extended to access data, including unstructured data, stored externally in a data lake. In this case, the external file is registered to the database, and a connector sends the request to the external data source.
Integrating a Data Lake With an Analytics Platform
Below are some approaches businesses have taken to connect their data lakes to a data analytics solution:
Traditional Data Warehouse
Moving data to a traditional data warehouse from a data lake ideally uses a data integration solution such as Actian DataConnect, which manages the data movements, transformations, and filtering needed to get the data into a suitable form for meaningful analysis.
Extended Data Warehouse
When the source data in the data lake is in a form that is ready for analysis, as is the case for many Hadoop data formats, an analytics technology can be beneficial. For example, the Actian Analytics Engine can use its built-in Spark connector to access more than 50 data formats, including Hadoop formatted file data. Likewise, the Actian Data Platform can host data warehouse projects and their required data integrations.
Data Lakehouse
The data lakehouse concept combines the data analysis capabilities of a data warehouse with the data lake function, which does not require a separate integration technology. A data lakehouse is a structured data repository stored in a database as tables and can also store semi-structured data formats such as JSON strings. Flat files store unstructured data such as video, audio, and text streams in one or more file systems. An integrated data catalog stores metadata that describes the data format, labels lineage, and more. Data connectors provide the means to access all the data types in the data lakehouse.
Data Integration Functions
Below are essential capabilities of data integration technology:
Data Connectors
Data lakes store a multitude of data types and file formats. The corresponding data integration solution needs connectors that encompass all of the required formats. Open database connectivity (ODBC) provides an open application programming interface (API) for simple formats. Spark connects to more complex data formats used by Hadoop File Systems. The ideal integration technology should provide the ability to build custom connectors if needed. Actian DataConnect supports hundreds of connectors and provides a universal connector for building connections to home-made applications.
Data Pipeline Orchestration
Actian DataConnect and KNIME offer visual workflow design tools for constructing data flows to move the data from the data lake to the target analytic system. Actian DataFlow plugs into KNIME to provide data transformation and analysis functions that can operate as multithreaded parallel operations to reduce execution times.
Scheduling
Integration solutions should provide a centralized view of all data pipelines, allowing IT to schedule and pause data movements.
Central Management
Integration solutions can monitor integrations, log exceptions, handle retries, and alert IT about failures.
Flexible Deployment
Data lakes can reside on-premises and on cloud platforms. A hybrid integration solution provides the most deployment flexibility.
Benefits of Cloud-Based Data Integration
The benefits of using a data integration solution with a data lake include:
- Makes data assets in the data lake easy to prepare for analysis.
- Provides ready-built connectors to hundreds of file formats, application APIs, and streamed data managers.
- Simplifies management of data pipelines through centralized monitoring and administration.
- Reduces administration costs thanks to being able to reuse scripts and having centralized visibility of data movements.
The data lakehouse architecture offers further benefits, such as providing a metadata catalog that describes formats, lineage, and how different data sets interrelate.
Actian and the Data Intelligence Platform
Actian Data Intelligence Platform is purpose-built to help organizations unify, manage, and understand their data across hybrid environments. It brings together metadata management, governance, lineage, quality monitoring, and automation in a single platform. This enables teams to see where data comes from, how it’s used, and whether it meets internal and external requirements.
Through its centralized interface, Actian supports real-time insight into data structures and flows, making it easier to apply policies, resolve issues, and collaborate across departments. The platform also helps connect data to business context, enabling teams to use data more effectively and responsibly. Actian’s platform is designed to scale with evolving data ecosystems, supporting consistent, intelligent, and secure data use across the enterprise. Request your personalized demo.
FAQ
Data lake integration is the process to move, prepare, and load data from a data lake for analysis in a data warehouse or analytics platform.
Integration technology makes stored data in a data lake helpful to the business by creating an automated path to an analytics system, enabling analysis of structured, semi-structured, and unstructured data.
A data lake stores data in its native format until needed for analysis and can hold all data types, while a data warehouse is ideally suited to analyze structured data that’s stored internally.
A data lakehouse combines the data analysis capabilities of a data warehouse with the data lake function, storing structured data as tables and semi-structured formats like JSON without requiring separate integration technology.
Essential capabilities include data connectors for multiple formats, data pipeline orchestration, scheduling, central management for monitoring and alerts, and flexible deployment across on-premises and cloud platforms.
Benefits include making data assets easy to prepare for analysis, providing ready-built connectors to hundreds of formats, simplifying pipeline management through centralized monitoring, and reducing administration costs through script reuse.
Businesses can use a traditional data warehouse with integration tools like Actian DataConnect, an extended data warehouse using built-in connectors like Spark, or a data lakehouse architecture that combines both functions.
The Actian Data Intelligence Platform unifies metadata management, governance, lineage, quality monitoring, and automation in a single platform to help organizations manage and understand their data across hybrid environments.
