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What are the best practices for extracting data from streaming and event-driven sources?

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Data extraction is the process of collecting, transforming, and loading data from various sources into a data warehouse or a data lake. Data extraction can be challenging when dealing with streaming and event-driven sources, such as web logs, social media, sensors, or IoT devices. These sources generate data continuously, at high volumes, and with varying formats and schemas. In this article, you will learn some of the best practices for extracting data from streaming and event-driven sources, such as:

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Sanjay Krishna

Data / AWS / LLMs

1 Choosing the right tools

There are many tools and frameworks available for extracting data from streaming and event-driven sources, such as Apache Kafka, Apache Spark, Apache Flink, Amazon Kinesis, Google Cloud Pub/Sub, and Azure Event Hubs. The choice of the tool depends on several factors, such as the data volume, velocity, variety, and quality, the scalability and reliability requirements, the integration and compatibility with other systems, and the cost and complexity of deployment and maintenance. You should evaluate the pros and cons of each tool and select the one that best suits your use case and budget.

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2 Designing a robust architecture

The architecture of your data extraction system should be designed to handle the challenges of streaming and event-driven data, such as latency, throughput, fault-tolerance, consistency, and security. You should consider using a microservices-based architecture, which allows you to decouple your data extraction logic from your data processing and storage logic, and to scale and update each component independently. You should also use a message broker or a queue system, such as Kafka or Kinesis, to buffer and distribute your data streams among multiple consumers, and to ensure that no data is lost or duplicated in case of failures or spikes.

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3 Applying schema evolution and validation

Streaming and event-driven data sources often have dynamic or evolving schemas, which means that the structure and format of the data can change over time. This can cause problems for your data extraction system, such as data loss, corruption, or inconsistency. To avoid these problems, you should apply schema evolution and validation techniques, such as using a schema registry, such as Confluent Schema Registry or AWS Glue Data Catalog, to store and manage the schemas of your data sources, and to enforce compatibility rules and versioning. You should also use a schema validation tool, such as Apache Avro or Google Protocol Buffers, to check and validate the data against the schemas before extracting it.

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4 Implementing data quality checks and monitoring

Data quality is essential for ensuring the accuracy and reliability of your data extraction system. You should implement data quality checks and monitoring mechanisms, such as using a data quality tool, such as Apache Griffin or Deequ, to measure and verify the completeness, correctness, timeliness, and consistency of your data. You should also use a data monitoring tool, such as Apache Airflow or Apache NiFi, to track and visualize the performance and status of your data extraction pipelines, and to alert and troubleshoot any issues or anomalies.

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5 Optimizing data formats and compression

Data formats and compression can have a significant impact on the efficiency and cost of your data extraction system. You should optimize your data formats and compression strategies, such as using a columnar or binary data format, such as Apache Parquet or Apache ORC, to reduce the size and increase the speed of your data. You should also use a compression algorithm, such as Snappy or Zstandard, to further compress your data and save storage and bandwidth. However, you should also consider the trade-offs between compression ratio and compression speed, and the compatibility and support of your data formats and compression methods with your data extraction tools and frameworks.

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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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Sanjay Krishna

Data / AWS / LLMs
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One thing that i did not see mentioned here is building fault tolerance in streaming applications. It is very essential to ensure fault tolerance through mechanisms like checkpointing and snapshotting to recover from failures without data loss. Additionally, adding backfill capabilities and ensuring order of events plays a crucial role as well in building a robust data extraction pipeline for streaming sources.

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What are the best practices for extracting data from streaming and event-driven sources?

1

2

3

4

5

6

1 Choosing the right tools

2 Designing a robust architecture

3 Applying schema evolution and validation

4 Implementing data quality checks and monitoring

5 Optimizing data formats and compression

6 Here’s what else to consider

Data Engineering

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What are the best practices for extracting data from streaming and event-driven sources?

1

2

3

4

5

6

1 Choosing the right tools

2 Designing a robust architecture

3 Applying schema evolution and validation

4 Implementing data quality checks and monitoring

5 Optimizing data formats and compression

6 Here’s what else to consider

Data Engineering

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