Data Engineering - Building a future-ready data architecture

In the seventh edition of the newsletter, I shared my views on how companies can make the significant shift to data-driven decision-making, and parallels with the Oil and Gas value chain (because Data is the new Oil!). In the newsletter, we introduced several important concepts in the Data value chain - Data Strategy, Data Engineering, Data Governance, Data Science and Data Visualization. In the eighth edition, we delved deeper into the data value chain, starting with Data Strategy. In this edition we continue with this journey with a deeper dive into Data Engineering.

What is data engineering and why does it matter?

Data engineering is the process of creating the data architecture for an organization. This includes designing, building, and maintaining systems to enable data-driven decision making and analytics.

The first role of Data Engineering is collecting, transforming, storing, and managing data from various sources, such as enterprise platforms (such as ERP, CRM), other applications (SaaS/ On-Premise/ Web), systems (Sensors/ IoT) and even external data sources. Data engineering then needs to create a Modern Data Platform (Data Lake, Data Lakehouse) through data pipelines and workflows for quality and governance. Finally, Data Engineering needs to curate and enrich data to be consumed by downstream applications including analytics, visualization, decision-support, customer/ user experience enabled by machine learning and artificial intelligence.

?Data engineering is essential for business leaders who want to gain a competitive edge in the digital economy. Data engineering enables business leaders to:

- Access and analyze large and complex data sets that provide valuable insights into customer behavior, market trends, operational efficiency, and business performance

- Automate and streamline business processes, such as marketing, sales, finance, and supply chain, by integrating data from different sources and systems, and applying rules, logic, and algorithms

- Innovate and create new products, services, and business models, by leveraging data to generate new ideas, test hypotheses, and validate outcomes

- Enhance and optimize customer experience, by using data to personalize content, recommendations, and offers, and to provide faster and more reliable service

- Improve and secure data quality, governance, and compliance, by using data engineering to ensure data accuracy, consistency, and availability, and to protect data from unauthorized access and misuse

How do we implement Data engineering in an organization?

Data engineering requires a strategic approach that aligns with your business goals, needs, and capabilities. It is also a complex and dynamic field that requires constant learning and adaptation.

Given the increasing data volume and complexity requirements, organizations are using a cloud-based platform such as Microsoft Azure or Google Cloud, to enable data engineering at scale, speed, and efficiency. They offer a comprehensive suite of data engineering services and tools for data ingestion, storage, processing, enrichment, and presentation. The below Microsoft Azure reference architecture diagram illustrates how to use data engineering services and tools in a typical scenario.

?The key layers in the architecture typically include the following:

• Source Systems - These include structured (e.g. relational databases, enterprise platforms, functional applications), semi-structured (e.g. xml/ json/ csv), unstructured (e.g. customer feedback, internet/ social media data, documents, multimedia), and streaming data (e.g. telemetry from IoT/ Sensors, monitoring data).
• Ingestion Layer - The ingestion layer accesses data and performs some validations and transformations necessary for storage (into databases as an example). Streaming data is typically analyzed in real time but stored/ processed/ actioned based on any 'events' identified. Events could be transaction completion, anomalies/ faults identified during monitoring, etc. Other data is generated and stored for processing using data pipelines to periodically ingest the data.
• Storage Layer -? This layer involves appropriately storing all data. For structured data, typically a Medallion architecture of Bronze (Raw), Silver (Cleansed), and Gold (Curated) is used to ensure data lineage and traceability. The Golden dataset is typically available in cubes required for downstream analytics. Semi-Structured and Unstructured data is stored with any optimizations such as compression if possible to save storage space/ cost.
• Processing Layer - In this layer, we conduct descriptive, exploratory, and predictive analytics as required based on the key questions identified during the data strategy. In some cases, it is required to combine information from multiple independent datasets to generate new insights.
• Enrichment Layer - The enrichment layer can perform multiple roles as needed including - matching/ deduplication of data, inferring missing information, auto-tagging of metadata for future analytics, as well as generating forecasts using AI/ ML.
• Consumption Layer - This is the final layer where data or insights are 'served' to business users. This can be in the form of data sets for custom analytics, process/ workflow interventions, standardized reports or flexible/ interactive dashboards. At this stage, all the data and insights combine to assist decision-making and generate tangible outcomes.

What are some good examples of data engineering?

Data engineering is a strategic advantage for business leaders who want to leverage data to drive innovation, efficiency, and growth in their organizations. Netflix, Airbnb, and Spotify are some of the leading companies that use data engineering to power their business models and customer experiences. Netflix uses data engineering to collect and analyze data from millions of users and devices, and to provide personalized recommendations, content, and features. Airbnb uses data engineering to collect and analyze data from millions of hosts and guests, and to provide customized listings, prices, and reviews. Spotify uses data engineering to collect and analyze data from millions of songs and listeners, and to provide tailored playlists, podcasts, and ads.

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“The goal is to turn data into information, and information into insight.”?– Carly Fiorina, former CEO, Hewlett-Packard Company

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