To succeed in this age of digital transformation, enterprises are embracing data-driven decision-making. And, making quality data available in a reliable manner proves to be a major determinant of success for data analytics initiatives. The data engineering teams straddle between building infrastructure, running jobs & fielding ad-hoc requests from the analytics and BI teams. And, this is where the data engineers are tasked to take into account a broader set of dependencies and requirements as they design and build their data pipelines.
But is there a way to structure it logically? Well, the answer is both yes and no. To start with you’ll need to understand the current state of affairs, including the decentralization of the modern data stack, the fragmentation of the data team, the rise of the cloud, and how all these factors have changed the role of the data engineering forever. And, how a proven framework with best data engineering practices can help tie the data pieces together to make decision-making seamless.
Through this article, based on our experience we’ll shed light on some of the data engineering best practices to enable you to work with data easier while delivering innovative solutions, faster.
The pointers listed below will help you build clean, usable, and reliable data pipelines, accelerate the pace of development, improve code maintenance, and make working with data easy. This will eventually enable you to prioritize actions and move your data analytics initiatives more quickly and efficiently.
Business data, whether it is qualitative or quantitative; can take different forms depending on how it is collected, created, and stored. You need the right tech stack, infrastructure, and processes in place to analyze it and generate accurate and reliable insights. Here’s a quick rundown on how to go about it;
ETL tools can efficiently move your data from the source to different target locations. They deliver the insights that your finance, customer service, sales, and marketing departments need to make smarter business decisions. But how do you choose the right tool? Listed below are some of the important criteria to evaluate an ETL tool as per your business need:
Data acquisition is an important process that deals with data discovery outside of the organization and to bring it within the system. The important aspect to consider here would be to glean the valuable insights you need from this information and how it will be used. And, that would require smart planning to ensure no time and resource gets wasted on the data that won’t be of use. Here are a few points based on our experience;
If the data ingestion has issues, every following stage suffers. Inaccurate data results in erroneous reports, spurious analytic results, and unreliable decisions.
While storage needs are specific to every enterprise, here are 6 key factors to consider when choosing the right data warehouse.
The future of data lies in the cloud and we’re competent in working with Azure & AWS. Lay a solid foundation of a sound data infrastructure that allows you to extract the right insights to deliver growth & transformation for your business.
With data warehousing (DWH) as a service, you can build a common data model irrespective of the data sources and enhance their visibility for informed decision-making. Plus, you get the added advantage of a cloud service that can scale and downsize as your business needs change.
As a part of our data engineering services, we help organizations to advance to the next level of data usage by providing data discovery & maturity assessment, data quality checks & standardization, cloud-based solutions for large volumes of information, batch data processing (with optimization of the database), data warehouse platforms and more. We help develop data architecture by integrating new & existing data sources to create more effective data lakes. Further, we can also integrate ETL pipelines, data warehouses, BI tools & governance processes.
With data engineering as a service, every business can accelerate value creation from data collected, extract intelligence to improve strategies & optimize analytics to drive real-time decisions. The listed best practices would enable making your data pipelines consistent, robust, scalable, reliable, reusable & production ready. And, with that data consumers like data scientists can focus on science, instead of worrying about data management.
Since this stream, doesn’t have a wide range of well-established best practices like software engineering – you can work with a data engineering partner and benefit from their experience. They can help you achieve these goals by leveraging the right tech stack, on-premises architecture, or cloud platforms & integrating ETL pipelines, data warehouses, BI tools & governance processes. This would result in accurate, complete & error-free data that lays a solid groundwork for swift & seamless adoption of AI & analytics.