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What are common data integration patterns for data lakes?

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Data lakes are large repositories of raw and unstructured data that can be used for various analytical purposes. However, to extract value from data lakes, you need to integrate data from different sources and formats, and ensure its quality, security, and accessibility. In this article, you will learn about some common data integration patterns for data lakes, and how they can help you achieve your data management goals.

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Melbin P.

Product Manager in Autonomous Robotics | Driving User-Centric AMR Solutions | Building Scalable Processes & Roadmaps…
Vignesh Coumarane

1 Batch integration

Batch integration is a data integration pattern that involves loading data from source systems into the data lake at regular intervals, such as daily, weekly, or monthly. This pattern is suitable for data sources that do not change frequently, or for scenarios where near-real-time analysis is not required. Batch integration can leverage existing ETL (extract, transform, and load) tools and frameworks, such as Apache Spark or AWS Glue, to perform data transformations and validations before loading the data into the data lake.

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2 Streaming integration

Streaming integration is a data integration pattern that involves ingesting data from source systems into the data lake as soon as it is generated or updated. This pattern is suitable for data sources that produce high volumes of data at high velocities, such as sensors, web logs, or social media. Streaming integration can enable real-time or near-real-time analysis and insights, as well as faster data availability and freshness. Streaming integration can use streaming platforms and frameworks, such as Apache Kafka or AWS Kinesis, to capture, process, and deliver data to the data lake.

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3 Change data capture

Change data capture is a data integration pattern that involves capturing only the changes that occur in the source systems, such as inserts, updates, or deletes, and applying them to the data lake. This pattern is suitable for data sources that have high update rates, or for scenarios where you want to maintain a consistent and synchronized view of the data across the source systems and the data lake. Change data capture can reduce the data volume and latency, as well as preserve the history and context of the data. Change data capture can use tools and frameworks, such as Debezium or AWS DMS, to monitor and capture the changes from the source systems.

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4 Federated integration

Federated integration is a data integration pattern that involves querying data from different sources and formats without moving or copying it to the data lake. This pattern is suitable for data sources that have strict security or compliance requirements, or for scenarios where you want to access the most recent and accurate data from the source systems. Federated integration can enable on-demand and flexible analysis, as well as avoid data duplication and storage costs. Federated integration can use tools and frameworks, such as Apache Presto or AWS Athena, to provide a unified query interface to the data sources.

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5 Hybrid integration

Hybrid integration is a data integration pattern that involves combining two or more of the above patterns to address different use cases and requirements. For example, you can use batch integration for historical data, streaming integration for real-time data, and federated integration for sensitive data. This pattern is suitable for complex and heterogeneous data environments, where you need to balance the trade-offs between performance, cost, quality, and timeliness. Hybrid integration can use a combination of tools and frameworks, such as Apache Airflow or AWS Step Functions, to orchestrate and coordinate the data integration workflows.

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Vignesh Coumarane
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Hybrid integration would be a better way to consume data involving a combination of batch and streaming approaches. Also, it can involve change data capture to import only the latest updated data. Having a simple batch integration will impair us from having real-time data and with just streaming and changing data capture - a limited amount of data can only be imported at a certain point in time. Having a hybrid approach solves the real problem. It also saves the business a ton of computing and transfer costs and time. This hybrid approach, combining batch and streaming, accommodates the diverse needs of data lakes, supporting both analytical depth and responsiveness to changing data landscapes.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Melbin P.

Product Manager in Autonomous Robotics | Driving User-Centric AMR Solutions | Building Scalable Processes & Roadmaps for Next-Gen Robotics
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1. Batch Processing: Scheduled large data movement, like nightly updates. 2. Change Data Capture (CDC): Tracking and moving only updated data parts. 3. Event-Driven Integration: Moving data based on events, triggered by notifications. 4. Data Replication: Copying data for consistency between systems. 5. Data Virtualization: Using data from sources without physically moving it. 6. Data Wrangling: Cleaning and preparing data before use. 7. Metadata Management: Organizing data information for easy identification. 8. Schema-on-Read and Schema-on-Write: Deciding when to structure data. 9. Federated Queries: Asking questions across multiple systems. 10. Data Cataloging: Creating a catalog for easy data discovery. 11. Data Quality Checks:

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What are common data integration patterns for data lakes?

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1 Batch integration

2 Streaming integration

3 Change data capture

4 Federated integration

5 Hybrid integration

6 Here’s what else to consider

Data Management

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What are common data integration patterns for data lakes?

1

2

3

4

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6

1 Batch integration

2 Streaming integration

3 Change data capture

4 Federated integration

5 Hybrid integration

6 Here’s what else to consider

Data Management

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