{"id":9665,"date":"2025-08-04T09:43:33","date_gmt":"2025-08-04T09:43:33","guid":{"rendered":"https:\/\/weareoakland.com\/?p=9665"},"modified":"2025-08-22T14:44:08","modified_gmt":"2025-08-22T14:44:08","slug":"advanced-data-engineering-with-databricks","status":"publish","type":"post","link":"https:\/\/weareoakland.com\/blog\/advanced-data-engineering-with-databricks\/","title":{"rendered":"The Role of Databricks Architecture in Data Engineering"},"content":{"rendered":"\n<p>Since it was founded in <a href=\"https:\/\/www.databricks.com\/company\/about-us\"><span>2013<\/span><\/a>, <a href=\"https:\/\/www.databricks.com\/\"><span>Databricks<\/span><\/a> has revolutionised enterprise data management and analytics. Built on Delta Lake, an open source storage format, the set of data engineering tools it prides itself on processing enormous amounts of data, then transforming them into datasets that are primed for exploration via machine learning (ML) models.<\/p>\n\n\n\n<p>At Oakland, anything and <strong>everything data<\/strong> is at the core of the data and AI consultancy services we provide. When it comes to data engineering, Databricks is a central block in how we build advanced <a href=\"https:\/\/weareoakland.com\/services\/data-platform\/\"><span>data platforms<\/span><\/a> to provide actionable data insights for clients.&nbsp;<\/p>\n\n\n\n<p>In this article, we dive into the details of <a href=\"https:\/\/weareoakland.com\/the-ultimate-guide-to-building-a-data-platform\/\"><span>building a data platform<\/span><\/a> with Databricks, including:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The role of Databricks in data engineering<\/li>\n\n\n\n<li>Why Databricks and ETL (extract, transform, load) are a match made in data heaven<\/li>\n\n\n\n<li>An overview of Azure Databricks<\/li>\n\n\n\n<li>The pros and cons of Databricks<\/li>\n\n\n\n<li>Use cases of Databricks<\/li>\n<\/ul>\n\n\n\n<p>Let\u2019s start.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-large-font-size\" id=\"h-the-role-of-databricks-in-data-engineering\">The Role of Databricks in Data Engineering<\/h2>\n\n\n\n<p>Databricks plays a central role in modern data engineering by providing a scalable, high-performance platform. Built on Apache Spark (which is around ten times faster than traditional SQL databases), it enables teams to ingest, transform, and process vast volumes of structured and unstructured data efficiently.&nbsp;<\/p>\n\n\n\n<p>With features like Delta Lake for reliable data storage, and Photon for accelerated SQL performance, Databricks powers robust ETL (extract, transform, load) pipelines, real-time processing, and advanced analytics. Its unified workspace supports collaboration across data engineers, analysts, and data scientists, making it a key component of enterprise data platforms.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-large-font-size\" id=\"h-databricks-and-etl-a-match-made-in-data-heaven\">Databricks and ETL: A Match Made in Data Heaven<\/h2>\n\n\n\n<p>As a cloud-based platform, Databricks lends itself to ETL workflows &#8211; in fact, several of its tools and features have been specially designed with ETL pipelines in mind. So if slicker data extraction, transformation, and loading is important to your data activities, a platform engineered using Databricks could be a perfect fit.&nbsp;<\/p>\n\n\n\n<p>Some of the benefits (and the features that enable them) are listed below:<\/p>\n\n\n\n<p class=\"has-large-font-size\">Easier ETL development:&nbsp;<\/p>\n\n\n\n<p>Thanks to Databricks Lakeflow Declarative Pipelines (previously Delta Live Tables), the operational complexities of ETL processes are automated. You define what <em>should <\/em>happen, not <em>how<\/em>, reducing boilerplate code and enabling ETL in SQL or PySpark, speeding up development cycles and reducing operational overhead. ETL can be written in Spark or SQL, too, for extra flexibility.<\/p>\n\n\n\n<p class=\"has-large-font-size\">Streamlined workflows<\/p>\n\n\n\n<p>ETL tasks, analytics, and machine learning pipelines are all orchestrated in Databricks Lakeflow Jobs (previously known as Databricks Workflows).<\/p>\n\n\n\n<p class=\"has-large-font-size\">More focus on <a href=\"https:\/\/weareoakland.com\/blog\/why-invest-in-data-quality\/\"><span>data quality<\/span><\/a><\/p>\n\n\n\n<p>Thanks to features like Lakeflow Declarative Pipelines and automated data quality (DQ) testing, Databricks reduces the need for engineers to manage pipeline infrastructure or check DQ, freeing up time to deliver high-quality data.<\/p>\n\n\n\n<h2 class=\"wp-block-heading has-large-font-size\" id=\"h-what-is-azure-databricks\">What is Azure Databricks?<\/h2>\n\n\n\n<p>Given its power, it was only a matter of time before Microsoft jumped on the Databricks capability. In <a href=\"https:\/\/www.databricks.com\/company\/newsroom\/press-releases\/databricks-delivers-microsoft-azure-databricks-addressing-customer-demand\"><span>2017<\/span><\/a>, they became a first-party provider of Databricks\u2019s cloud-base platform, integrating it with its own Azure cloud services. The result? <em>Azure Databricks<\/em>, the open analytics platform that allows you to build, deploy, share, and maintain enterprise-grade data, analytics, and AI solutions at scale.&nbsp;<\/p>\n\n\n\n<p>Naturally, as a <a href=\"https:\/\/weareoakland.com\/partners\/\"><span>Microsoft Partner<\/span><\/a> awarded the <a href=\"https:\/\/weareoakland.com\/press\/oakland-achieves-microsoft-analytics-on-microsoft-azure-specialisation-strengthening-its-data-and-ai-expertise\/\"><span>Analytics on Microsoft Azure specialisation<\/span><\/a>, we were super excited about the integration! Azure Databricks is another building block in our data engineering toolkit, allowing us to engineer data platforms at enterprise scale. Not to mention the immense potential it\u2019s opening up for our customers and their <a href=\"https:\/\/weareoakland.com\/blog\/taming-your-data-assets-with-databricks\/\"><span>data assets<\/span><\/a>.<\/p>\n\n\n\n<p>Our blog, \u2018<a href=\"https:\/\/weareoakland.com\/blog\/how-to-create-a-secure-azure-data-platform\/\"><span>How to create a secure Azure data platform<\/span><\/a>\u2019, looks at Azure services in more detail.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>\u201cOur collaboration with Microsoft builds on our momentum as a leading cloud platform for Apache Spark-based analytics. The ability to provide our Unified Analytics Platform to all Microsoft Azure users in such an integrated fashion is invaluable to end users looking to simplify big data and AI.\u201d&nbsp;<\/p>\n\n\n\n<p><strong>Ali Ghodsi, Co-Founder and CEO of Databricks<\/strong><\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading has-large-font-size\" id=\"h-databricks-use-cases\">Databricks Use Cases<\/h2>\n\n\n\n<p>With this in mind, let\u2019s cut to three of our recent use cases using Databricks as part of our advanced <a href=\"https:\/\/weareoakland.com\/services\/data-platform\/\"><span>data engineering service<\/span><\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading has-large-font-size\" id=\"h-1-building-a-sustainable-long-term-data-platform-for-network-rail\">1. Building a sustainable, long-term data platform for Network Rail<\/h3>\n\n\n\n<p>Data platform engineering is an investment, so you need to make sure the technology is set up for <a href=\"https:\/\/weareoakland.com\/guides\/unlocking-your-data-future\/\"><span>future success<\/span><\/a>. Databricks enables an open approach, reducing the complex nature of being \u2018locked in\u2019 that comes from using a more traditional platform vendor. Something our client, Network Rail, knew all too well.<\/p>\n\n\n\n<p>Like many other large organisations with legacy data platforms, Network Rail was struggling to access data, which made extending the capabilities of their datasets difficult. Using Databricks, we built an open <a href=\"https:\/\/weareoakland.com\/blog\/what-is-data-platform-architecture\/\"><span>data platform architecture<\/span><\/a> for the rail services provider, which has:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Automated manual processes, freeing up valuable time and resources to spend elsewhere<\/li>\n\n\n\n<li>Reduced lead times by merging two loosely data pipelines into one<\/li>\n\n\n\n<li>Enabled smarter decision-making, thanks to the deployment of advanced analytics and ML tools<\/li>\n<\/ul>\n\n\n\n<p>Yet that\u2019s just the start &#8211; Click here to read the full case study.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/weareoakland.com\/case-studies\/business-intelligence-reporting-analytics\/\"><span>Case study: Network Rail<\/span><\/a><\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading has-large-font-size\" id=\"h-2-informing-sales-strategies-for-a-leading-provider-of-it-infrastructure\">2. Informing sales strategies for a leading provider of IT infrastructure<\/h3>\n\n\n\n<p>Sales team struggling to extract data from multiple sources? We recognise the challenge, and it\u2019s one we helped a leading provider of IT infrastructure overcome.&nbsp;<\/p>\n\n\n\n<p>After we designed the IT service firm\u2019s new <a href=\"https:\/\/weareoakland.com\/services\/data-analytics-and-insights\/\"><span>data analytics<\/span><\/a> platform, we leveraged the Databricks stack to build a machine learning and data science model. Their sales team now have access to far richer insights, driving better margins for the overall business. These insights include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>A customer\u2019s tendency to buy certain product categories&nbsp;<\/li>\n\n\n\n<li>A highlighting system of the products that customers are likely to buy<\/li>\n\n\n\n<li>Product penetration and available spend information, so staff can quickly spot where to focus time and energy<\/li>\n<\/ul>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/weareoakland.com\/case-studies\/helping-a-leading-provider-of-it-infrastructure-unlock-sales-growth-with-a-150-roi\/\"><span>Read the case study<\/span><\/a><\/div>\n<\/div>\n\n\n\n<h3 class=\"wp-block-heading has-large-font-size\" id=\"h-3-driving-an-roi-increase-of-150m-for-yorkshire-water\">3. Driving an ROI increase of \u00a3150m+ for Yorkshire Water<\/h3>\n\n\n\n<p>As part of an overall data transformation programme, we developed a new data platform for Yorkshire Water. Databricks was primed to be the enterprise data architecture for the utilities company and was pivotal to the design and build of their new, strategic data platform.&nbsp;<\/p>\n\n\n\n<p><strong>In total, ROI from the overall business transformation has exceeded \u00a3150m.<\/strong><\/p>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/weareoakland.com\/case-studies\/yorkshire-water-data-strategy\/\"><span>Case study: Yorkshire Water<\/span><\/a><\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading has-large-font-size\" id=\"h-what-are-the-pros-and-cons-of-databricks\">What are the Pros and Cons of Databricks?<\/h2>\n\n\n\n<p>It\u2019s fair to say our Databricks and Azure Databricks use-cases and results speak for themselves. However, it\u2019s important to weigh up the pros and cons of any data architecture to make sure you\u2019re choosing the best fit for your business needs. We\u2019ve outlined some of the major pros and cons of Databricks below to give you a better understanding of whether it\u2019s right for you or not.<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Pros<\/strong><\/p>\n\n\n\n<p class=\"has-text-align-left\">Advanced <a href=\"https:\/\/weareoakland.com\/services\/data-governance\/\"><span>data governance<\/span><\/a> capabilities, such as data lineage, roles, and permissions thanks to an in-built Unity Catalogue<\/p>\n\n\n\n<p>Ease of scaling and maintenance<\/p>\n\n\n\n<p>One unified platform for batch, streaming, ML, AI, and analytics<\/p>\n\n\n\n<p>Native integration with all major cloud platforms and PaaS, plus native DevOps and Git support<\/p>\n\n\n\n<p>Eliminates data silos by using <a href=\"https:\/\/weareoakland.com\/blog\/should-you-use-data-lakehouse-instead-of-a-data-warehouse-and-or-data-lake\/\"><span>Data Lakehouse<\/span><\/a> architecture<br><br>Provides a collaborative approach to Data Warehousing in a database<\/p>\n\n\n\n<p>The ETL process is in-built and in one place, omitting the need for another tool<\/p>\n\n\n\n<p>Features are continuously updated and added\u00a0<br><\/p>\n\n\n\n<p><br><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-vertically-aligned-stretch is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Cons<\/strong><\/p>\n\n\n\n<p>Cost, especially at scale<\/p>\n\n\n\n<p>Higher barrier to entry for non-developers<\/p>\n\n\n\n<p>More suitable for bigger datasets<\/p>\n\n\n\n<p>Constantly evolving product, so you need to have the time and resources to dedicate to understanding these changes<\/p>\n<\/div>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading has-large-font-size\" id=\"h-data-engineering-advice\">Data Engineering Advice<\/h2>\n\n\n\n<p>Of course, for more advice on Databricks and data engineering, or to speak to us about your needs for a data platform, please get in touch with our friendly team. That\u2019s what makes us Oakland, everything data.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Since it was founded in 2013, Databricks has revolutionised enterprise data management and analytics. Built on Delta Lake, an open source storage format, the set of data engineering tools it prides itself on processing enormous amounts of data, then transforming them into datasets that are primed for exploration via machine learning (ML) models. At Oakland,&#8230;<\/p>\n","protected":false},"author":8,"featured_media":9662,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[406],"tags":[78,365,444,445],"class_list":["post-9665","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-platform-data-engineering","tag-data-engineering","tag-data-platform","tag-data-platform-engineering","tag-platform-engineering"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v27.2 (Yoast SEO v27.2) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>The Role of Databricks Architecture in Data Engineering | Oakland<\/title>\n<meta name=\"description\" content=\"An overview of building a data platform with Databricks, from its role in data engineering to why Databricks and ETL are a perfect match.\" \/>\n<meta name=\"robots\" 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