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Data Ingestion

1. Overview

1.1. Definition:

Data ingestion refers to the process of transporting data from various sources to a storage medium where it can be accessed, used, and analyzed.

1.2. Sources of Data:

  • Data can be ingested from diverse sources such as databases, cloud storage, APIs, IoT devices, and social media platforms.

1.3. Data Formats:

  • Data ingestion tools must handle multiple data formats, including structured, unstructured, and semi-structured data formats like JSON, CSV, XML, Avro, Parquet, etc.

1.4. Types

1.4.1. Temporality:

  1. Batch Processing
    • Definition: Data is collected over a period, then processed as a single unit or batch.
    • Latency: Typically associated with high latency, as it waits for a complete dataset before processing.
    • Use Cases: Ideal for scenarios where up-to-date data is not crucial, such as end-of-day reporting, ETL processes, and periodic data integrations.
    • Scalability: Generally scalable for large volumes of data, since processing can be done in bulk.
    • Complexity: Simpler to implement in comparison to streaming, often utilizing traditional databases and data warehouses.
  2. Streaming Processing
    • Definition: Data is processed in real-time or near-real-time as it arrives.
    • Latency: Low latency, providing immediate or timely processing of information.
    • Use Cases: Suited for applications requiring instant data processing like fraud detection, live event monitoring, and online recommendation systems.
    • Scalability: Can handle continuous data flows which may require distributed processing systems to scale effectively.
    • Complexity: More complex to implement due to the requirement of managing data flow, consistency, and processing order.
  3. Connections and Considerations:
    • Data Volume & Velocity: Batch is preferable for high-volume, less frequent transactions, whereas streaming better handles continuous flows of data.
    • Data Consistency & Accuracy: Consider how eventual consistency or exactly-once semantics would impact your application; these are more challenging to guarantee in streaming systems.
    • Infrastructure & Cost: Streaming might require more sophisticated and potentially costly infrastructure to maintain low latency.
    • Business Needs: Analyze whether the nature of your business operations aligns more closely with periodic updates or ongoing, real-time data insights.

1.4.2. Mechanisms

  1. Push
    • Definition: Data is sent from the source to the destination proactively.
    • Use Cases: Suitable for real-time or near-real-time data applications.
    • Advantages:
      • Lower latency since data is sent as soon as it's available.
      • Simplicity for the source as it only needs to send data to the target.
    • Disadvantages:
      • More complex error handling required by the destination to manage unexpected data arrival.
      • Potentially more challenging to scale if the source needs to send data to multiple destinations.
  2. Pull
    • Definition: The destination requests and retrieves data from the source.
    • Use Cases: Ideal for periodic batch data processing.
    • Advantages:
      • The destination controls the rate and timing of data retrieval, simplifying error management and processing.
      • Easier to manage retries and failed data retrievals.
    • Disadvantages:
      • Higher latency, as data is retrieved based on the destination's schedule.
      • Increased complexity on the destination side, as it must implement scheduling and data checking mechanisms.
  3. Connections and Considerations
    • Latency: Push systems generally have lower latency than pull systems since they send data immediately upon availability.
    • Scalability: Pull systems might offer better scalability if multiple consumers are polling from the same source, while push systems can become complex if the source pushes data to many destinations.
    • Resource Management: Push systems require proactive resource management by the source, while pull systems require it by the destination.
    • Error Handling: Pull systems often have built-in mechanisms to handle intermittent retrieval failures, while push systems require robust error-handling frameworks at the

1.5. Challenges in Data Ingestion:

  • Scalability: Managing increasing volumes of data efficiently.
  • Data Quality: Ensuring the accuracy and consistency of data being ingested.
  • Latency: Minimizing delays from data source to destination.
  • Security: Protecting data during ingestion from unauthorized access or corruption.

1.6. Best Practices:

  • Ensuring data quality and cleansing before ingestion.
  • Implementing robust error handling mechanisms.
  • Using scalable solutions that can adapt to growing data inflows.
  • Monitoring the ingestion process continuously to detect and fix issues early.

2. Misc

2.1. Key Engineering Considerations for Ingestion Phase

  • What are the use cases for the data I'm ingesting?
    • Can I use this data rather than creating multiple version of the same dataset?
  • Are the systems generating and ingesting this data reliably, and is the data available when I need it?
  • What is the data destination after ingestion?
  • How frequently will I need to access the data?
  • In what volume will the data typically arrive?
  • What format is the data in? Can my downstream storage and transformation systems handle this format?
  • Is the source data in good shape for immediate downstream use? If so, for how long, and what may cause it to be unusable?
  • If the data is from a streaming source, does it need to be transformed before reaching its destination? Would an in-flight transformation be appropriate, where the data is transformed within the stream itself?
Tags::cs:data: