Como otimizar o desempenho e a eficiência dos trabalhos de integração de dados em lote?

Distributed Processing: Opt for tools that support distributed processing frameworks like Apache Spark or Hadoop to handle large volumes of data across multiple nodes, accelerating processing. Parallelism: Choose tools that can parallelize tasks within a job, leveraging multiple cores or machines to process data concurrently. Optimized Algorithms and Data Structures: Select tools that employ efficient algorithms and data structures designed for large-scale data processing. Caching: Look for tools that support caching mechanisms to reduce the need to read and write data repeatedly, enhancing overall performance.

The foundation of efficient batch data integration lies in selecting the appropriate tools and technologies. Modern ETL tools like Apache NiFi, Talend, or Informatica are built to handle high volumes of data, offering features like parallel processing, scheduling, and error handling. When working with cloud-based infrastructures, tools such as AWS Glue, Azure Data Factory, or Google Dataflow provide scalability and integration with other cloud services. It’s crucial to evaluate factors like data volume, complexity, and team expertise when choosing tools, ensuring they align with the technical requirements and future growth of the data ecosystem.

Durante a modelagem de dados, é essencial você identificar e deixar claro quem é sua tabela fato, juntamente com as tabelas dimensões. Com uma boa definição, os dados vão ser colocados nos campos certos e o acesso deles será mais lógico

A well-designed data model is crucial for optimizing the performance and efficiency of batch data integration jobs. By carefully structuring your data and considering the relationships between entities, you can significantly improve data processing speed, reduce storage requirements, and enhance overall efficiency. Eliminate Redundancy: Normalization involves decomposing tables to remove redundant data. This reduces storage space and minimizes the risk of inconsistencies during data updates. Improve Query Performance: Normalized tables are typically smaller and more efficient to query, especially when joining multiple tables.

Also, consider having the loadtimestamps at every stage of the entire process, including the audit tables explicitly. This will be handy in terms of post production/tracebacks/ root cause analysis

An optimized data model is critical for performance and scalability in batch processing. Normalize data where it reduces redundancy and improves consistency but denormalize when quick joins and aggregations are needed, especially for read-heavy workloads. Indexing key columns and partitioning large datasets can also dramatically improve query performance during batch jobs. Furthermore, understanding the nature of the data—whether it's transactional, historical, or aggregated—helps in designing storage formats like Parquet or ORC, which are efficient for analytical queries. A well-structured data model reduces the need for extensive transformations and ensures smooth batch execution.

Adjust the size of data batches to strike a balance between throughput and resource utilization. Larger batches can reduce overhead, but overly large batches may strain resources or increase latency. Experiment with different batch sizes to find the optimal balance.

Try to automate ETLs wherever possible e.g: extracting from source to staging - metadata driven approaches can be leveraged. With little more complicated logic, even some of the data transformations can be automated. These come handy, esp. when we have to extract hundreds/thousands of tables from the source Also, will help in reducing the TCO esp. when the development efforts + maintainence efforts are taken in to consideration

Efficient ETL logic is the heart of batch job. Minimize unnecessary transformations by extracting and transforming the data required for the target use case. Leverage bulk operations and push-down predicates to reduce data movement and processing time. Where possible, parallelize tasks to make use of multi-threaded or distributed processing capabilities of your ETL tool. Implement incremental loads instead of full refreshes to handle only changed data, reducing time and resource usage. Monitoring and tuning key parameters like memory allocation, batch size, and I/O throughput are equally essential to prevent bottlenecks. By carefully considering tools, data models, and ETL logic, you can ensure batch data integration jobs are efficient

Also, leverage the audit/monitor logs to figure out : a) patterns in the data size and what days, we got more data (e.g monthly first week/ quarter last week) so that optimal scheduling can be done to make proper usage of the hardware (be it source or sink side) b) raise alerts if there is sudden drop/ raise in the volume/rows of data c) proactively identify the potential sizing that may be required for future

Some of the tools give option of retry + long retries. In case the tool does not give, Plan to have back up schedules, so that for any reasons it gets failed- still there is the backup schedule. Ensure that the backup schedule does extract only when the original schedule didnt work. Also, please be careful in the schedules esp when there are different source spread across different timezones and the sink in different timezone. The daylight savings related clock changes can have impact