How can you effectively choose a data ingestion technique?

electing a data ingestion technique in data engineering involves considering factors like data volume, velocity, and variety. For large, non-time-sensitive datasets, batch processing may be suitable, while real-time streaming is ideal for dynamic, time-critical data. Prioritize scalability, fault tolerance, and compatibility with existing systems. Ensure security and compliance with regulations to meet industry standards. Evaluate costs, resource requirements, and processing speed, aligning the chosen technique with specific use cases and overarching business objectives. This holistic approach ensures an effective data ingestion strategy that balances efficiency, scalability, and business needs in the dynamic field of data engineering.

?The necessity is "business areas need to get data more updated to work" but the aggravating factors may be either "when data is generated in data source" or "with x time range to extract" without deadline of response to data product. The ingestion frequency without deadline of response is main key to choose it; and difference with real time ingestion. It will work with several data chunks to process; so you need to use high storage volumen to store these chunks temporally, and need processing strategy to process it.

?? In my experiences; when you need get one ingestion infrastructure in storages and processor servers, use a stabled budget with possible to pay extra working, as well standardized tech stack tools, knowledge and language: unification, stability and simplification ?? Some drawbacks are it becomes a bottleneck for data ingestion process, possible conditioning in the deployment of new changes and don't improve specified tasks with differences tech tools.

In my experience, it is important to assess 1. whether business requires data real-time or batch 2. business and IT is aware about expected finops of selected ingestion technique e.g. business use cases doesn't need real time data but data engineer uses dataflow as ingestion technique and this increases finops

In my data field expertise, choosing the right data ingestion technique plays major role and here are my observations. 1. Data Sources - Assess nature (structured/unstructured) and volume. 2. Purpose - Define goals for analytics, real-time processing, or storage. 3. Data Quality and Availability - Determine quality standards, compliance, and expected data availability. 4. Processing Requirements - Identify transformation and enrichment needs. 5. Consumption Considerations: - Understand data consumption, prioritizing latency and throughput. 6. Governance Priorities: - Implement security and privacy measures, aligning with standards. A strategic lens is vital for adept data ingestion in the dynamic data world.

Data platforms generally tend to use a kappa or lambda architecture design to run workloads depending on the use case, frequency of the data being ingested and the SLA defined with the downstream users. Both architectures got theirs benefits. I lean towards a kappa architecture on stream ingest layer with a batch process (ELT), as it reduces redundancy and also presents with the flexibility to decouple the pipeline events to either batch or stream processing depending on the business needs, like for example, data related to ML events such as anomaly detection are relayed from stream ingest directly, whereas rollup and BI related data is processed from the datalake in batch process. This helps in optimizing costs more granularly.

1. Understand Your Data: Know its source and format—tables, files, or real-time streams. 2. Consider Data Volume: Evaluate scalability for handling varying data amounts. 3. Speed of Data Delivery: Assess if real-time or delayed data suits your needs. 4. Data Transformation Needs: Check for flexibility in transforming data during ingestion. 5. Handling Issues: Plan for robust error handling to ensure data integrity. 6. Ease of Management: Opt for straightforward setup and monitoring. 7. Cost Evaluation: Examine budget considerations for data ingestion. 8. Integration with Tools: Ensure seamless compatibility with existing data tools. 9. Security Compliance: Verify adherence to security rules and data laws for protection.

Clearly define your data ingestion objectives, including the data sources, destination systems, data volumes, frequency of ingestion, and desired level of data transformation.