How do you optimize data engineering for large and complex systems?

Understanding the overall context of the business, user requirements, existing technology, new technology, and cross-functional dependencies are key to narrowing down the objectives and requirements for implementing any data engineering project. The requirements are never constant but the core business seldom changes. The data architecture and framework should be designed such that changing requirements do not require major changes to the framework. Defining the scope of a data engineering project is difficult as there are a number of tools and technologies, disparate sources, and a large number of stakeholders. These challenges can be overcome if one has a strong grasp of business knowledge and technology fundamentals.

we can consider the below to optimize the data engineering solutions... Data Modeling: Design an efficient data model. Partitioning/Sharding: Distribute data wisely. Indexing: Use indexes for query speed. Parallel Processing: Leverage parallel frameworks. Data Pipelining: Automate ETL with pipelines. Monitoring: Identify and address bottlenecks. Caching: Cache frequently accessed data. Distributed Databases: Consider distributed solutions. Resource Scaling: Scale resources dynamically. Cost Management: Monitor and reduce costs.

Data Governance, Quality To build data engineering for complex systems, aspects of data which includes data governance, data quality, data observability should also be taken into account in the initial phases. The cost of adding them at a later stage is much higher.

It's important to have an understanding of all business unit functions that make up high-level company objectives. A clear data governance strategy that defines a collection of unique Data domains that are used to logically map new and existing data sources in to an integrated Enterprise data model that can support current and future business requirements can help reduce tech debt coming from maintaining redundant datasets. As the business gets faster, underlying data assets grow increasingly more disconnected from reality due to poor governance and lack of requirements analysis before development of data pipelines. With the new developments in AI/LLMs, i predict its going to be more about how good your data is than how big it is.

Once we know "what" should be done then follows "how". Choosing the right tool and technology matters as we shouldn't compare an Elephant to a Fish. They both specialize in their own area. For a parquet file processing in an integration task, if a proprietary ETL tool consumes a huge amount of space for data aggregation to remove duplicates then it is better to do the same with Python to save a lot of compute resources. Understanding the need and carefully deciding between the trade-offs of different tools will help in identifying the appropriate tool / technology for the use-case.

Best practices implementation should be part of your design irrespective of the business user requirements: 1. Build a design for your data pipeline that has enough security and governance checks added. For example usage of vaults , secrets , parameters to mask text values or PI values in your automated pipeline. 2. When building a pipeline a complete flow and Architecture needs to be developed ahead of time which can be later modified to have a logical picture and clear understanding of what you are building is as per requirement and not missing best practices. 3. If it’s a community development of pipeline via code or using ETL tools it always needs to have an option of scalability , re-use ability, enough monitoring checks in design.

There are several ways to optimize the performance of data engineering pipelines. Some of the most essential points to be considered are 1. Data Storage Layout: How the data is stored physically in storage (file formats, partitioning, size of files, etc) 2. Parallelism of the jobs: This could be achieved by right sizing the clusters (assuming we are considering distributed compute systems) and setting the right job configurations. 3. Best practices on the query: This is often overlooked and is a major point of improvement in most cases. Filtering out data which is not required and bringing less data to be processed in memory often results in great performance improvements.

Create data engineering leaders by mentoring emerging engineers. Sharing the best practices based on different industries will definitely help engineers avoid costly mistakes. Teaching will help one reinforce the fundamentals, architectures, and frameworks. There are a lot of mentoring platforms that can be used to leverage this great objective.