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What do you do if you discover performance gaps during data engineering evaluations?

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Discovering performance gaps during data engineering evaluations can be unsettling, but it's an essential part of optimizing data systems. As a data engineer, you're tasked with building and maintaining robust data pipelines that efficiently process and move data. When evaluations reveal that these systems aren't performing as expected, it's crucial to identify the root causes and take corrective action. This article will guide you through the steps to address these performance gaps, ensuring your data infrastructure is both reliable and scalable.

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Mohammad Areeb

Ex-Microsoft | Azure Data Engineer |Microsoft Fabric | SQL | KQL | Power BI | ADF | Databricks | Azure Synapse |…
Yaswanth Kumar

Python Full stack and Data Engineer

1 Identify Issues

When you uncover performance gaps, the first step is to pinpoint the specific issues causing them. Analyze system logs, query execution plans, and resource utilization metrics to identify bottlenecks. Look for patterns such as slow-running queries, excessive memory usage, or disk I/O operations that could indicate inefficiencies. Understanding the nature of the problem is pivotal before you can work on a solution.

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Mohammad Areeb

Ex-Microsoft | Azure Data Engineer |Microsoft Fabric | SQL | KQL | Power BI | ADF | Databricks | Azure Synapse | Python | AI Services | Azure OpenAI | MDM | Intune
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Upon finding performance gaps during data engineering evaluations, it’s crucial to diagnose the root causes. Analyze system logs, query performance, and resource usage to identify bottlenecks. Investigate slow queries, high memory use, or intensive disk operations. Once you understand the issues, optimize queries, upgrade hardware, or refactor data models as needed. This targeted approach ensures efficient data processing and system performance.

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Yaswanth Kumar

Python Full stack and Data Engineer
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Identify the queries which are taking longer to execute, understand which joins or conditions are taking more time. Even if the table has huge data, it will take more time to fetch data. Remove unnecessary joins and optimise queries as much as possible. You can also optimise the queries by splitting a single query into multiple queries. Instead of using multiple joins in a single query, split them, this will improve the performance of your query.

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Deepak Chowdary Nama

MSCS @ Stony Brook University | Looking for Summer Internships 2025 | Data Engineer | IT Business Analyst | Software Engineer | Product Manager | Microsoft Certified: Azure Developer Associate | Adobe Student Ambassador
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The shift in the overall work run time is crucial. We need to divide and analyze the following if it occurs frequently and takes 1.5 times longer than expected: changes in data volume and schema, query execution time, memory spillovers, I/O operations taking longer than expected, etc.

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Milka Wafula (She/her) ??

Data-Driven Problem Solver ||Mentor|| Big Data Engineer ||ML Engineer ||Co-Founder of Women in Machine Learning, Data Science Mauritius, Kenya ||WTM Ambassador ||BSc (Hons) #technologyforsocialimpact.
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Analyze Root Causes: Conduct a thorough analysis to identify the root causes of the performance gaps. Evaluate factors such as infrastructure limitations, inefficient processes, inadequate resources, or skill gaps within the team. Collect Performance Metrics: Gather relevant performance metrics and data to quantify the extent of the performance gaps. This could include metrics such as data processing speed, system response times, resource utilization, or data quality metrics.

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2 Root Analysis

Once you've identified the issues, conduct a root cause analysis to understand why they're occurring. This may involve reviewing the design of your data models, the configuration of your data processing engines, or the code of your ETL (Extract, Transform, Load) processes. Sometimes, the cause might be external, such as network latency or hardware limitations. Accurate diagnosis is critical for effective remediation.

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3 Plan Fixes

With a clear understanding of the root causes, you can now plan fixes to address the performance gaps. This may include optimizing SQL queries, redesigning data models for better performance, or upgrading hardware. In some cases, you might need to restructure your ETL processes or adjust configurations for your data storage and processing systems. Create a detailed action plan that outlines each step needed to resolve the issues.

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4 Implement Changes

After planning, it's time to implement the changes. Start with the least disruptive fixes and monitor the impact on system performance. For significant changes, such as hardware upgrades or major refactoring of data pipelines, consider using a staged approach to minimize downtime and risk. Thorough testing is vital to ensure that your modifications achieve the desired performance improvements without introducing new issues.

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5 Monitor Results

Post-implementation, it's crucial to monitor the results to confirm that the performance gaps have been closed. Use the same metrics and monitoring tools that helped you identify the issues initially. Look for improvements in processing times, resource utilization, and overall system stability. Continuous monitoring will also help you catch any unforeseen problems early and allow you to make further adjustments as needed.

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6 Iterate Process

Finally, remember that data engineering is an iterative process. Even after addressing the current performance gaps, new challenges will arise as data volumes grow and requirements change. Maintain a proactive approach to performance evaluation and be ready to repeat this cycle as part of your ongoing efforts to optimize data systems. Your goal is to create a scalable and efficient infrastructure that can adapt to future demands.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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SALMAN KHAN

AVP at Axis Bank | Business Intelligence, Data Engineering & Architecture | Doctoral Scholar in Business Administration & Analytics
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I would recommend the below steps to address performance gaps discovered in data engineering evaluations: - Pinpoint bottlenecks using logs and monitoring tools. - Optimize data processing with parallelization and efficient algorithms. - Enhance data quality management to minimize reprocessing. - Implement caching and indexing for faster data access. - Regularly refine ETL jobs and automate repetitive tasks. - Upgrade infrastructure or adopt cloud solutions for scalability. - Monitor performance continuously and adjust strategies as needed. It is important to keep in mind that this can be an iterative process so it is important to be patient.

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What do you do if you discover performance gaps during data engineering evaluations?

1

2

3

4

5

6

7

1 Identify Issues

2 Root Analysis

3 Plan Fixes

4 Implement Changes

5 Monitor Results

6 Iterate Process

7 Here’s what else to consider

Data Engineering

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What do you do if you discover performance gaps during data engineering evaluations?

1

2

3

4

5

6

7

1 Identify Issues

2 Root Analysis

3 Plan Fixes

4 Implement Changes

5 Monitor Results

6 Iterate Process

7 Here’s what else to consider

Data Engineering

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