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.
Data Engineering Best Practices
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.
- Analysis of Source Data
- ETL Tool Evaluation
- Data Acquisition Strategy
- Storage Capability – Centralized & Staging
- Data Warehousing
Analysis of Source Data
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;
- Assess Data Needs & Business Goals: Gain a clear understanding of how you would approach big data analytics at the very outset. The type of data you will collect, where it will be stored, how it will be stored, and who will analyze it – everything needs to be planned.
- Collect & Centralize Data: Once you have a clear understanding of your data needs, you need to extract all structured, semi-structured, and unstructured data from your vital business applications and systems. This data should then be transferred to a data lake or a data warehouse. This is where the ELT or ETL process will come into play.
- Perform Data Modeling: For analysis, data needs to be centralized in a unified data store. But before transferring your business information to the warehouse, you may want to consider a data model. This process will help you determine how the information is related & how it flows together.
- Interpret Insights: You can use different analytical methods to uncover practical insights from business information. You can analyze historical data, track key processes in real-time, monitor business performance and predict future outcomes.
ETL Tool Evaluation
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:
- Pre-built Connectors and Integrations
- Ease of Use
- Scalability and Performance
- Customer Support
- Security and Compliance
- Whether you want to go for Batch Processing or Real-Time Processing
- ETL or ELT
Data Acquisition Strategy
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;
- One-click Ingestion: Movement of all existing data to a target system. All analytics systems and downstream reporting tools rely on a steady stream of accessible data. One-click ingestion allows you to ingest data in different formats into an existing table in the Azure Data Explorer and create mapping structures.
- Incremental Ingestion: The incremental extract pattern allows you to extract only changed data from your source tables/views/queries, reducing the load on your source systems and overall ETL duration. To determine the incremental ingestion type that meets your need, you need to consider the format, volume, velocity, and access criteria of your source data.
If the data ingestion has issues, every following stage suffers. Inaccurate data results in erroneous reports, spurious analytic results, and unreliable decisions.
Storage Capability – Centralized & Staging
While storage needs are specific to every enterprise, here are 6 key factors to consider when choosing the right data warehouse.
- Cloud vs. On-prem: If most of your mission-critical databases are on-premises and not compatible with cloud-based data warehouses. Otherwise, you wouldn’t want to take on the stresses & strains that accompany on-prem infrastructure.
- Implementation Cost & Time: All vendors have radically different calculations for computing power, storage, configurations, etc. So, do your due diligence on the pricing info. You also need to factor in the cost of the team handling the implementation. When you’re weighing the implementation time, make sure that your chosen data warehouse does not take months to implement. Cost is a decisive factor, but time is more crucial. A moderately costly data warehouse can prove to be insanely expensive if you wait longer to get the insights needed to outwit your competitors!
- Tech Stack: If your business has invested heavily in a specific data tech stack and does not have a major chunk of information residing outside of it, then picking that ecosystem’s tech stack makes sense. For instance, if most of your solutions have an SQL Server backend and need a custom integration, odds are you’ll go with Azure!
- Scalability: If you’re a fast-growing enterprise, you need to determine the current volume of data, how likely it is to grow, and if the data warehouse can expand with your growing business needs.
- Ongoing Costs and Maintenance: Your ongoing costs can far outweigh the resources you allocate upfront. The costs you need to consider include staff time spent on performance tuning, storage and compute resources, and data warehouse maintenance cost
- IT Support: Make sure to verify that your preferred tool comes with an online community and live support that is included in the pricing tier. Having instant access to IT support for prompt handling of IT issues is a real lifesaver.
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.
- Categorize Business Processes Using a Bus Matrix: A bus matrix is both a project artifact and a design tool that simplifies the representation of the subject areas and dimensions associated with your DWH. It acts as a guide to the design phase and provides a mechanism for communicating business information back in the overall architecture. It serves many purposes from communicating requirements, capabilities, and expectations with the business users down to the prioritization of tasks.
- Define the Granularity: The grain defines the lowest level of detail for any table in the DWH. If a table contains daily marketing data, then it should be daily granularity. If it contains the sales data for each month, then it has monthly granularity. At this stage, you need to find answers to questions like:
- Do you need to store sale information on a daily, weekly, monthly, or hourly basis? This decision is based on your reporting needs.
- Do you need to store your entire product portfolio or just a few categories? This decision is based on the key business processes
- Identify Dimensions and Attributes: Dimensions typically include specific details like products, dates, inventory, and store location. This is where all the data gets stored for a given duration which may range from a week, a month, or a year. Attributes are the different characteristics of the dimension in data modeling. In a store location dimension, the attributes can be state, zip code, and country. They are typically used for searching and classifying facts.
- Identify Facts: This step is closely associated with business users as they get access to all the stored data in the warehouse from the fact table rows. Facts are numerical values like cost per unit, price per unit, etc. they help determine the sales for different product categories across locations daily.
- Star Schema: An arrangement of tables in a manner that enables an accurate analysis of business performance. The star schema architecture resembles a star with the ends radiating from a central point. The center contains the table of facts, and the ends comprise dimension tables.
Rishabh’s Data Engineering Mix
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.