ETL tools for Big Data

Switchboard Aug 31

Spotlight on – ETL Tools
Table of Contents

    As a data unification concept originating in the 1970s, ETL has had to adapt to a world where the volume of unstructured data has skyrocketed. Here, we take a look at how a modern ETL process is applied to big data.

    How is ETL done for Big Data?

    The demands for availability, combined with the sheer scale of data used, has presented challenges for ETL in Big Data. Conventional pipelines are built to process data in batches, but modern users expect real-time availability. The pipeline must be ready to run queries, generate reports, and perform data analysis – all while new data is being received.

    To mitigate bottlenecks, a streaming data pipeline can be used to transfer data continuously. But using this approach with traditional tools can result in a lack of scalability or conflicts between incoming queries and record updates.

    Therefore, more sophisticated ETL tools for Big Data are needed.

    Which tool is best for ETL?

    ETL tools are pieces of software that facilitate ETL pipelines, and there are several different types. Enterprise tools are more expensive, but benefit from commercial support. Open source tools are free and customizable, but come with no support or guarantee. You could even develop your own ETL tool, the advantage being that the software is bespoke to your needs, but this requires considerable internal resources. Most recently, cloud ETL tools have become available which provide greater availability and elasticity. However, these platforms don’t typically manipulate datasets stored in other locations, so they must first be transferred into the cloud.

    The ETL tools list is vast, so there is no single panacea which solves all problems in Big Data. However, there are certain attributes which the best ETL tools all have. Here are the top ten:

    1. Excellent credential management – each source will require its own particular credentials to access data, so these will need to be accessible, secured, and easy to manage.
    2. Comprehensive integrations – the best ETL tool will be compatible with every API you require.
    3. Maximal automation & scheduling – since ETL can involve hundreds of integration jobs per day, automating as much as possible will save time.
    4. High-performance quality – software quality consists of three main elements: accuracy, stability, and speed. Accuracy is the minimization of errors, stability is consistent behavior without crashing, and speed is how fast it performs.
    5. Both on-premises and cloud-based – The option to deploy your ETL tool either on your own servers or in the cloud.
    6. Built-in data profiling – before running ETL, you’ll need to examine in detail the source datasets carefully to determine structure, quality, and integrity.
    7. Data governance – the collection of processes and policies which ensure the quality and security of the data used by an organization.
    8. Security – If your datasets contain sensitive information, this must be encrypted, and access permissions applied appropriately.
    9. Monitoring & alerting – when Big Data is involved, ETL requires a number of different data pipelines. There are many reasons why these may fail during processing, so your ETL tool needs to be able to monitor activity and alert you when this happens.
    10. Ease of use – It’s always nice when your software has an intuitive interface and is easy to use. For example, it’s desirable to have simplified functions and drag-and-drop capabilities.

    How Python is used in ETL

    Python is a fundamental programming language with many applications, but it has a number of useful modules for manipulating databases, so finds widespread use in ETL. Among ETL tools, Python is often used to connect other pieces of software or even build open-source ETL tools.

    Here’s a brief tutorial demonstrating how Python is used for ETL:

    1. Hire engineers – You’ll need these to complete an entire ETL pipeline.
    2. Procure the credentials – These are needed to access the data sources, and must be provided to your Python environment.
    3. Write the extraction code – Connect the disparate data sources and extract data from each format, such as XML, JSON, and CSV.
    4. Write the cleaning code – This cleans and standardizes the raw data.
    5. Write the transformation code – This applies the required transformations to the data.
    6. Write the loading code – Load data into the target destination (usually a data warehouse).
    7. Perform ETL testing – Deploy your pipeline securely, and test its output and performance.
    8. Set up monitoring and alerting – This will inform you of any failures, and enable you to manage server resources and scaling.

    If you need help unifying your first or second-party data, we can help. Contact us to learn how.

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