How do you optimize the performance and efficiency of batch data integration jobs?

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.

Durante a modelagem de dados, é essencial você identificar e deixar claro quem é sua tabela fato, juntamente com as tabelas dimens?es. Com uma boa defini??o, os dados v?o 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

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

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