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    Data Ingestion Framework

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    High-Performance Data Ingestion for Modern Analytics Platforms
    Overview

    High-Performance Data Ingestion for Modern Analytics Platforms

    The Advanced Data Ingestion Framework is a high-performance framework with which you can connect enterprise data to your cloud of choice. Leveraging Speed, scalability and reliability, it simplify complex data pipelines. It powers workflows through automation, real-time ingestion, and scheduled ingestion. Organizations can get faster insights with complete traceability and operational control.

    The Problems It Solves

    As the business grows, pipelines become complex which pose managing and scalalbility issues

    With today's enormous data, processing becomes slower which results in reporting and analytical delays

    Data can be missing, duplicated or inorrect during transfers

    Lack of automation make the data ingestion process rely heavily on human interventions

    Difficulty in debugging failures as no transparency into failures and location of data

    Data is distributed across multiple systems instead of being in one place

    What Does the Product Do?

    Connects diverse on-premise and cloud data sources to analytics platforms

    Automates ingestion through scheduled and event-based workflows

    Orchestrates complex pipelines through a configurable interface

    Ensures high data integrity with built-in validation and error handling

    Provides end-to-end traceability from source to destination

    Scales to handle large enterprise data volumes

    Frequently asked questions

    • What kind of data sources are supported?
      The Framework is designed to help extract data from relational databases and REST APIs. The supported relational databases include MySQL, Oracle, SQL Server and BigQuery, both on-prem and in cloud environments. The framework supports full and incremental data extraction from relational databases, depending on the logic of data extraction. It can also help to extract data from REST APIs and load the extracted data into the target system. It's flexible enough to work with several data sources and can be reused in various data integration situations. Streaming sources including Pub/Sub will be supported by future changes.
    • Does it support real-time ingestion?
      The current implementation has the capability to extract data from relational databases or APIs in batches and in a scheduled manner. Cloud Composer is used to orchestrate workflows and they run on schedules that can be set. If the relational database is used, incremental extraction is supported; if an API is used, it can be called based on the scheduled execution. Implementing ingestion from sources like Pub/Sub stream or real-time isn't yet supported.
    • How does it guarantee the reliability of the data?
      The framework includes both validation and error handling features that can be programmed and execution logging features that provide detail to ensure reliable data extraction. It saves metadata of all extraction jobs, checks source and target data, and logs data about the execution status, record count and detailed error information. When it comes to relational databases, incremental extraction only processes new or updated data, minimizing data duplication. Retry mechanisms and audit logs improve reliability, and make troubleshooting easier.
    • Does it support big data for enterprise?
      Yes. The framework is optimized for the processing of a large amount of enterprise data. It uses Google Cloud Dataflow for scalable processing, Cloud Storage for intermediate staging when required and BigQuery for high-performance storage and analytics. A large relational data set can be efficiently managed with parallel processing and configuration of strategies for data extraction without compromising performance and reliability.
    • Is manual intervention required?
      No. After source configurations, extraction rules, API information, scheduling and metadata are configured, the framework will automatically run the extraction workflows through Cloud Composer. It doesn't need a lot of manual running; only users are engaged when adjustments to the configuration or exception handling are needed.
    • Are there monitoring and visibility features?
      Yes. It provides full monitoring capabilities, and provides detailed execution logs and metadata for each pipeline execution. It contains details of execution status, timestamps, source and target details, no. of records pulled, API status and error messages. This enables to have a 100% traceability, monitoring of operations and fast troubleshooting.
    • Can workflows be customized?
      Yes. The framework is metadata-centric, with user-configurable metadata that include relational database connections, API endpoints, authentication information, extraction queries, incremental load strategies, scheduling frequency, target destinations, and validation rules, without making any changes to application code. This flexible architecture will allow the same framework to be used for more than one database and API extraction scenario and will increase the reusability and reduce the amount of development time.
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