There are many things can be checked for this situation. First one is checking query plans to realize that if there is something to reduce performance, like outdated statistics, fragmented indexes or parameter sniffing or etc. can also monitor resources like cpu for high utilisation and memory for free space and disk for iops. Also sometimes customers behaviour changes (like black friday,...) and you must consider that maybe you need to change your pipeline to cover them.

Put on your Sherlock Holmes hat and start with the basics: 1. For each pipeline - what was the run time when it was acceptable vs. today 2. For pipelines that are now slower - break down each step duration in the past vs. now 3. Check if there were changes made prior to the slowness - code, infrastructure, timing, etc. 4. Once you've isolated your offending task/s within those pipelines, prioritize fixing them based on business needs rather than slowest run time. 5. Avoid going down rabbit holes. Before you rush towards redesigning the pipeline - consider the value, effort, team bandwidth and risks. Stable pipelines are better than erretic ones :)

Improving ETL performance can involve various strategies, depending on the tools, data size, and specific requirements. Optimize Data Sources(Filter at the Source, Use Incremental Loads), Leverage Parallel Processing(Multi-threading, Partitioning), Efficient Data Transformation(Pushdown Transformations, Use In-Memory Processing), Optimize Data Storage(Compression, File Formats, Indexing), Pipeline Design Optimization(Eliminate Bottlenecks, Batch vs. Stream Processing, Simplify Logic), Improve Network and I/O Performance(Data Locality, Parallel File Transfers), Leverage Cloud Features (If Applicable)(Autoscaling, Managed Services) ,Error Handling and Retry Logic.

⚙️ Boosting ETL Performance: Troubleshoot Like a Pro 🚀 Slow ETL pipelines? Don’t let them bottleneck your workflows! Here’s how to tackle performance issues: 📊 Monitor Resources: Check CPU, memory, and disk I/O for constraints slowing processes. 🔍 Optimize SQL Queries: Use indexed columns, streamline joins, and avoid redundancies. ⚡ Parallelize Tasks: Break processes into smaller, concurrent tasks for faster execution. 🛠️ Proactive tuning = smoother pipelines. What are your go-to strategies for optimizing ETL performance? Let’s discuss! 💬 #ETLPerformance #DataPipelines #OptimizationTips #BigData #Troubleshooting

pipelines may run slow, because CPU, RAM are used by processes of analysts, tools like Aplitude, Tableau. Often casual analysts have no time and knowledge to optimize queries, so very expensive constructions may occur: joins without filtration, window_fuctions, calculating field that already exists in some table, and occur regularly. this cases should be found, and user educated to write more optimal queries. 1) find unefficient queries with DB logs 2) outline most promlematic cases 3) give communication with advices in case of Clickhouse check table system.query_log columns memory_usage, query_duration_ms, table system.processes column elapsed (execution_time_sec)

To troubleshoot slow ETL pipelines, I’d: Analyze bottlenecks: Use monitoring tools to identify slow stages (e.g., extraction, transformation, or load). Optimize queries: Check for inefficient SQL queries or large data scans. Scale resources: Evaluate infrastructure (CPU, memory, I/O) and scale if necessary. Partition data: Implement partitioning or indexing to handle large datasets efficiently. Review workflows: Ensure parallelism, streamline data flows, and remove unnecessary transformations.

To troubleshoot and improve ETL performance effectively: Monitor Resource Usage: Check CPU, memory, disk I/O, and data shuffling for bottlenecks. Optimize Queries: Use indexing, avoid unnecessary joins, and filter data early. Leverage Parallelism: Break tasks into smaller parallel units and optimize partitions. Use Efficient Formats: Switch to Parquet/ORC for faster read/write and compression. Minimize Data Movement: Process data close to its source and avoid redundant transfers.

Use some debugger tools to figure out from where slowness is coming from and then let's work on the part of ETL which is slow.. sometimes slowness is from 3P systems or networking delays which are out of control, we can do parallel batch executions if possible, also sometimes implementing a queue can also be a good solution to do batch processing.

Firstly you need to analyze the system architecture to identify potential bottlenecks. Use monitoring tools to pinpoint stages causing delays, such as data extraction, transformation, or loading. This will help prioritize each of the stage’s weakest parts. Optimize data handling by partitioning large tables for parallel processing and filtering out unnecessary data to reduce load. Enhance processing efficiency through parallel processing and caching frequently accessed data. Review and refine ETL code to ensure it follows best practices, and assess network latency, optimizing as needed to improve performance.

To troubleshoot slow ETL pipelines, I would: 1. Monitor performance using tools like Spark UI or CloudWatch to identify bottlenecks. 2. Check for data skew and optimize partitioning or bucketing. 3. Review and optimize SQL queries for unnecessary joins or non-indexed operations. 4. Ensure adequate resource allocation (CPU, memory, disk). 5. Increase parallelism by adjusting threads or partitions. 6. Cache intermediate datasets to reduce recomputation. 7. Use efficient data formats (Parquet/ORC) with compression to minimize I/O overhead. 8. Minimize redundant I/O operations by optimizing storage layout. 9. Profile code to pinpoint inefficiencies. 10. Audit and adjust job scheduling to avoid resource contention.