What are the best practices for choosing a data ingestion framework?

Having a complete understanding of your usecase and the behavior of your Data is one of the first steps when choosing a Data ingestion framework. Here are few other points to consider - - Data characteristics like volume, velocity, format, source - Ease of use, i.e. Support with existing infrastructure (cloud, databases, analytics tool) - Scalability, as in the framework continues to support the data needs with time

When choosing a data ingestion framework, consider scalability, flexibility, ease of use, reliability, real-time capabilities, security, community support, ecosystem integration, cost, monitoring, and future-proofing. This comprehensive approach will help select a framework that meets specific needs and enhances operational efficiency.

When choosing a data ingestion framework, start with a clear understanding of your unique use case. What's the story of your data? Is it a steady stream needing real-time analysis, or is it more about volume and variety? Think about your data's origin - from IoT devices to APIs - and where it needs to go. This isn't just about technology; it's about your business goals. Are you driving real-time decisions, or are you feeding a complex analytical machine? Your choice should fit like a glove to your specific narrative, not just now, but as your data's story unfolds.

Some of the aspects you need to consider: 1- Assess data sources format and its compatibility with the ingestion framework 2- Ability to handle incremental ingestion 3- Ability to handle schema evolution 4- Data quality checks 5- Monitoring of data ingestion 6- Autoscaling when we have burst of data 7- real-time vs batch ingestion

"The ideal data ingestion framework should seamlessly handle three distinct stages: pre-ingestion, during-ingestion, and post-ingestion. During the pre-ingestion stage, it is essential to meticulously finalize business requirements and identify various types and sources of data. In the during-ingestion phase, emphasis should be placed on efficient data management and scalability of the framework, utilizing streaming techniques or other appropriate data transfer methods. Finally, the post-ingestion framework should focus on validating data consistency, ensuring persistence, and defining subsequent actions as necessary."

The batch ingestion process is typically scheduled to occur at regular intervals. This could be every few hours, once a day, or even once a week, depending on the specific requirements. Batch ingestion is particularly useful when you want to query across large data with minimal latency or to test out new features using a simple data file. It’s also advantageous for repeatable processes.

?? Selecting the ingestion mode shapes framework selection. For real-time needs, opt for streaming frameworks offering low-latency processing. Batch processing demands tools like Apache Spark or Flink for extensive data analysis. Tailor the mode to your data velocity and processing requirements for optimal results! #IngestionMode #RealTimeProcessing #BatchProcessing #DataVelocity #DataProcessingModes

Batch ingestion processes data in predefined chunks at scheduled intervals, suitable for non-time-sensitive applications ??. On the other hand, real-time ingestion ensures immediate data flow, ideal for time-sensitive analytics and rapid decision-making ??. Consider your business requirements, data velocity, and processing capabilities before making a choice. Sometimes a hybrid approach blending both batch and real-time ingestion might offer the best of both worlds ??. Remember, the right mode sets the foundation for effective data management and analysis, so choose wisely! ?????

When selecting a data ingestion framework, it's crucial to evaluate your options wisely ??. Start by identifying your specific requirements and understanding the nature of your data sources. Consider factors such as scalability, compatibility, and ease of integration. Explore frameworks that align with your technology stack and support your data formats. Prioritize solutions that offer robust error handling and monitoring capabilities ???. Additionally, assess the community and vendor support for ongoing maintenance and updates. A thorough evaluation ensures you choose a framework that seamlessly fits your needs and sets the foundation for efficient data processing and analytics ??.

?? Evaluating frameworks entails weighing features and ecosystem support. Consider factors like scalability, fault tolerance, and community support. Apache Beam offers portability, while Apache Flume excels in log data handling. Scrutinizing these factors aids in choosing the most fitting framework! #FrameworkEvaluation #Scalability #FaultTolerance #CommunitySupport #DataEngineering #FrameworkSelection

It should also be noted that we will not always have 100% compatibility between our infrastructure and the tool we choose, even the most complex ones. We must have this on our radar because developing our own infrastructure in an IaC model can guarantee us not only maximum compatibility, at least in data capture, but also versatility in processing since metadata can be defined and modified immediately after capture (in order to guarantee a record of the data being ingested).

While different options are available and seem feasible, one should look into wider use cases within the organization for data ingestion. Having to chose a framework that is being used in other project/product, could be a wise decision, in terms of development efforts, unless there are specific requirements to move away. An example could be where ADF does all the ingestion works from different sources, mainly SQL databases and/or streaming data connecting ADLS. If there are requirements on quality checks/de-duplication etc, then Spark Structure Streaming is the way to go.

??? Testing frameworks validates their suitability. Conduct proof-of-concepts or pilot implementations. Assess performance, ease of integration, and adaptability. Iterating through testing phases refines framework selection, ensuring compatibility with your infrastructure and requirements! #FrameworkTesting #ProofOfConcept #PerformanceTesting #DataIngestion #DataEngineering

When selecting a data ingestion framework, the key to success lies in the "Test and Iterate" mantra ??. Begin by identifying your organization's specific needs and goals, then run pilot tests with a subset of your data. This allows you to evaluate how well the framework aligns with your requirements and uncover any potential challenges. Iterate based on the insights gained during testing, adjusting configurations and parameters as needed. Embrace an agile approach, continuously refining your choices to ensure optimal performance and scalability. By incorporating regular testing and iteration into your selection process, you pave the way for a robust and tailored data ingestion solution that aligns seamlessly with your business objectives ??

Lastly, it's crucial to document and review your data ingestion framework and its outcomes meticulously. Document the framework's design, implementation, and operation along with the insights and learnings gained. Regularly review and update this documentation. Sharing these details with stakeholders—your data engineering team, consumers, and providers—fosters transparency, accountability, and ensures the framework's quality. This practice not only upholds the framework's integrity but also allows for continuous enhancements based on collective insights and feedback.

Document all aspect in process: ?? Infrastructure architecture: How many nodes are using? ?? Capacities about lifecycle software: What the flexibility, scalability, development and maintenance capacity level that tool give the data team? ?? Cases of usage: How use case is designed in architecture and solution view? ?? Evaluation criterias: What points will be evaluate to get a score and compare with other tools: time of execution, time of development, time of maintenance, etc? ?? Results individual and in group: How were the results of the tools when applying the evaluation criteria?

When it comes to documenting and reviewing your options, it's all about laying out the canvas ??. Start by clearly defining your data sources and formats. ?? Documenting these details helps you understand the landscape you're working with. Next, review frameworks with a keen eye, considering factors like scalability, flexibility, and ease of integration ??. Just like an art critic assesses brush strokes, evaluate the framework's ability to handle real-time and batch data ??. Document your findings, noting strengths and weaknesses. This documentation becomes your exhibit guide, aiding future decisions and ensuring your data masterpiece stands the test of time ??.

?? Documenting the selection process is key. Record insights, challenges, and reasoning behind framework choices. Regularly review framework performance and evolving needs. Comprehensive documentation aids in refining strategies and sharing insights with teams, fostering informed decisions! #DocumentationProcess #FrameworkReview #DecisionReview #DataEngineering #KnowledgeSharing #BestPractices

One pattern that’s been very helpful for us has been to push the vast majority of our processing to our data warehouse (Snowflake). When we started, we thought our data volumes would be small enough to perform all processing and normalization in Airflow. We assumed scaling our Airflow cluster would be sufficient. What we didn’t anticipate were the many technical problems-to-be-solved caused by scaling up workers, multi-threading, and other performance-focused optimizations. Since then, we’ve pushed all of that work into our data warehouse. We avoid thinking about multi-threading and scalability (Snowflake makes billions doing just that, so why reinvent the wheel?), and still deliver all the business value needed of our system!

There are many other things to consider, I'll list a few: - Data security and compliance: make sure the framework complies with data security and regulatory standards for your organization. - Scalability: Can you handle growing data volumes without compromising performance? - Community support: Is it a framework widely used or relatively new? Are the questions and answers in public forums or online resources? - Integration capabilities: How well does the framework integrate with your current data infrastructure? - Costs: Understand all the costs related to the framework, from licensing to maintenance as well as operational.

I presume that data granularity is sufficient for operations but most of IT guys have neglected financial / regulatory reporting and it’s disclosure note requirement. This gap must be closed to work it out with finance and regulatory teams. In addition, we have to consider the forthcoming new financial and regulatory reporting standard.