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How do you design data pipelines for historical data sources?

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Data pipelines are workflows that automate the extraction, transformation, and loading (ETL) of data from various sources to a destination for analysis or consumption. Historical data sources are those that contain data from the past, such as archives, backups, logs, or snapshots. Designing data pipelines for historical data sources can be challenging, as they may have different formats, schemas, quality, and volume than current data sources. In this article, we will discuss some best practices and common patterns for designing data pipelines for historical data sources.

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Lasha Dolenjashvili

Data Solutions Architect @ Bank of Georgia | Analytics Engineer & IIBA? Certified Business Analyst | Open to Freelance,…

1 Identify the purpose and scope of the historical data

The first step in designing data pipelines for historical data sources is to identify the purpose and scope of the historical data. What are the business or analytical questions that the historical data can answer? How much historical data is needed to provide meaningful insights? How often does the historical data need to be refreshed or updated? These questions will help you define the requirements and expectations for the data pipeline, as well as the trade-offs between complexity, performance, and cost.

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Lasha Dolenjashvili

Data Solutions Architect @ Bank of Georgia | Analytics Engineer & IIBA? Certified Business Analyst | Open to Freelance, Remote, or Relocation Opportunities
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1. Determine what data is required - utilize stakeholders' expertise. Even if business stakeholders might not be well-versed in data, their input is invaluable. Engaging them ensures a comprehensive understanding of the data needs. 2. Ask about data retention - it denotes the duration for which various data objects should be preserved. 3. A related question is the treatment of outdated data. Should it be archived for potential future use, or deleted to conserve storage and maintain data relevance? 4. Refresh/Update schedule - while stakeholders might frequently request near real-time data, it's crucial to assess the actual necessity. 5. Finally, there's the matter of data that isn't immediately required. Should we archive or delete it?

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2 Choose the appropriate data pipeline architecture

The next step is to choose the appropriate data pipeline architecture for the historical data sources. Depending on the nature and volume of the historical data, you may opt for a batch, streaming, or hybrid architecture. Batch architecture is suitable for historical data sources that are static, large, and infrequent, such as backups or snapshots. Streaming architecture is suitable for historical data sources that are dynamic, small, and frequent, such as logs or events. Hybrid architecture is suitable for historical data sources that are mixed, variable, and unpredictable, such as archives or social media.

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3 Select the right data pipeline tools and technologies

The third step is to select the right data pipeline tools and technologies for the historical data sources. Depending on the type and format of the historical data, you may need different tools and technologies for extracting, transforming, and loading the data. For example, you may use tools such as Apache Spark, Apache Kafka, or Apache Airflow for processing large-scale historical data in a distributed and parallel manner. You may also use tools such as AWS Glue, Azure Data Factory, or Google Cloud Dataflow for orchestrating and managing data pipelines in a cloud environment.

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4 Implement data quality and governance measures

The fourth step is to implement data quality and governance measures for the historical data sources. Historical data sources may have issues such as missing values, outliers, duplicates, inconsistencies, or errors that can affect the accuracy and reliability of the data pipeline outputs. Therefore, you need to implement measures such as data validation, cleansing, standardization, enrichment, and deduplication to ensure the quality and integrity of the historical data. You also need to implement measures such as data lineage, metadata management, access control, and auditing to ensure the compliance and security of the historical data.

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5 Test and monitor the data pipeline performance

The final step is to test and monitor the data pipeline performance for the historical data sources. You need to test the data pipeline functionality, scalability, reliability, and efficiency to ensure that it meets the requirements and expectations for the historical data. You also need to monitor the data pipeline metrics, logs, alerts, and feedback to ensure that it operates smoothly and effectively for the historical data. You may use tools such as Apache Airflow, Apache NiFi, or Apache Atlas for testing and monitoring data pipelines in a comprehensive and visual way.

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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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Rahul Dawn

Senior Data Engineer at Tesco Technology | PySpark | Hadoop
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It's important to understand your data as a first step. Mind mapping of the source data and destination data is necessary. The type of data, data model, size of data, transformations overview are supposed to be understood.

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How do you design data pipelines for historical data sources?

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4

5

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1 Identify the purpose and scope of the historical data

2 Choose the appropriate data pipeline architecture

3 Select the right data pipeline tools and technologies

4 Implement data quality and governance measures

5 Test and monitor the data pipeline performance

6 Here’s what else to consider

Data Engineering

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How do you design data pipelines for historical data sources?

1

2

3

4

5

6

1 Identify the purpose and scope of the historical data

2 Choose the appropriate data pipeline architecture

3 Select the right data pipeline tools and technologies

4 Implement data quality and governance measures

5 Test and monitor the data pipeline performance

6 Here’s what else to consider

Data Engineering

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