data ingestion architectural approach

Question

What is the sound technical strategy and approach for data ingestion design?

What are the important considerations?

Answer 1

Since you didn't provide any information and your question very general , I think that when designing a data ingestion strategy with Azure Data Lake Storage and Azure Data Factory, it's crucial to understand the data's types, sources, and formats, and to select the appropriate ingestion method (batch or real-time). Integration with ADLS should be seamless, ensuring scalability, optimal performance, and alignment with security standards. Key considerations include data quality, cost management, latency requirements, integration with other Azure services, disaster recovery, and metadata management. The choice between real-time and batch processing and proper monitoring, logging, and alerting helps create an efficient, robust, and compliant data ingestion pipeline.

Answer 2

Hello @Anshal Just checking if we are still connected on this discussion? Please let us know if you need further assistance in this matter? Are good to close this case?

Adding few more points to Amira's previous response, hope this is helpful..!

When designing a data ingestion architecture, there are several important considerations to keep in mind. Here are some key factors to consider:

1. Data sources: Identify the data sources that you need to ingest data from. This could include databases, files, APIs, and other sources.
2. Data volume: Determine the volume of data that you need to ingest. This will help you determine the appropriate data ingestion tools and technologies to use.
3. Data frequency: Determine how frequently the data needs to be ingested. This will help you determine the appropriate scheduling and automation tools to use.
4. Data quality: Determine the quality of the data that you need to ingest. This will help you determine the appropriate data validation and cleansing tools to use.
5. Data transformation: Determine if any data transformation is required before the data is ingested. This could include data mapping, data enrichment, and data aggregation.
6. Data storage: Determine where the ingested data will be stored. This could include databases, data lakes, and other storage solutions.
7. Data security: Determine the security requirements for the ingested data. This could include data encryption, access controls, and other security measures.
8. Data governance: Determine the governance requirements for the ingested data. This could include data lineage, data cataloging, and other governance measures.
9. Scalability: Determine the scalability requirements for the data ingestion architecture. This will help you determine the appropriate tools and technologies to use to ensure that the architecture can handle increasing data volumes and frequency.
10. Monitoring and alerting: Determine the monitoring and alerting requirements for the data ingestion architecture. This will help you identify and address any issues that arise during the data ingestion process.

By considering these factors, you can design a sound technical strategy and approach for data ingestion that meets your organization's specific needs and requirements.

Data ingestion with Azure Data Factory

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