{"id":7724,"date":"2026-08-07T10:54:35","date_gmt":"2026-08-07T08:54:35","guid":{"rendered":"https:\/\/revodata.nl\/?p=7724"},"modified":"2026-08-07T14:42:31","modified_gmt":"2026-08-07T12:42:31","slug":"what-is-databricks","status":"publish","type":"post","link":"https:\/\/revodata.nl\/nl\/what-is-databricks\/","title":{"rendered":"What is Databricks?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"7724\" class=\"elementor elementor-7724\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-93ac9ae elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"93ac9ae\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-62f0001\" data-id=\"62f0001\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a7904a0 elementor-widget elementor-widget-text-editor\" data-id=\"a7904a0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p data-pm-slice=\"1 1 []\">Databricks is a cloud-based data and AI platform. It helps organizations collect, prepare, govern, analyze, and use data for reporting, machine learning, AI applications, and operational decision-making. For decision-makers, the simplest explanation is this: Databricks gives data teams one shared environment to work with large amounts of data and turn that data into trusted analytics and AI. Instead of maintaining separate platforms for data engineering, data warehousing, data science, machine learning, and governance, Databricks brings those capabilities together on one open foundation. That matters because many organizations have the same problem: data is spread across systems, teams use different tools, AI initiatives struggle with data quality, and reporting definitions are inconsistent. Databricks is designed to reduce that fragmentation.<\/p><p><strong>Want to understand whether Databricks matches your organization? <a href=\"https:\/\/revodata.nl\/databricks-consultancy\/\">RevoData can help you define a focused proof of concept with clear business value, technical scope, and success criteria.<\/a><\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8b8ac19 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8b8ac19\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-98a3ee2\" data-id=\"98a3ee2\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5d34679 elementor-widget elementor-widget-text-editor\" data-id=\"5d34679\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">What is Databricks?<\/h2><p>Databricks is a unified platform for data, analytics, and AI. It is used by data engineers, analysts, data scientists, machine learning engineers, AI engineers, and business teams that need reliable insights from large and complex datasets. The platform started as an open-source data engineering ecosystem. Databricks was founded in 2013 by people behind major open-source data technologies, including Apache Spark, Delta Lake, and MLflow. Today, the platform has expanded beyond big data processing into data warehousing, governance, real-time processing, AI development, application development, and data sharing.<\/p><p>A practical way to understand Databricks is to compare it with a factory for data and AI:<\/p><ul><li><p>Raw data comes in from applications, files, databases, sensors, APIs, and cloud storage<\/p><\/li><li><p>Data engineers clean, structure, and combine it<\/p><\/li><li><p>Governance controls who can access which data<\/p><\/li><li><p>Analysts use it for dashboards and SQL reporting<\/p><\/li><li><p>Data scientists build models<\/p><\/li><li><p>AI teams build assistants, agents, or prediction systems<\/p><\/li><li><p>Business users consume the results through dashboards, applications, or automated processes<\/p><\/li><\/ul><p>A pipeline tool and a governance tool sitting next to each other don&#8217;t help much if someone still has to manually hand off results between them. The real value comes from connecting data engineering, analytics, AI, and governance into one operating model, so a dataset built once can move straight into a dashboard, a model, or an application without a manual handoff in between.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-662e48c elementor-widget elementor-widget-text-editor\" data-id=\"662e48c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">What problems does Databricks solve?<\/h2><p>Most organizations do not have a shortage of data. They have a shortage of usable, trusted, and well-governed data. Common symptoms of this include:<\/p><ul><li><p>Reports that show different numbers for the same metric<\/p><\/li><li><p>Data teams spending too much time on manual preparation<\/p><\/li><li><p>AI pilots that cannot move into production<\/p><\/li><li><p>Cloud costs that are hard to explain<\/p><\/li><li><p>Data stored in separate systems with unclear ownership<\/p><\/li><li><p>Slow access to new datasets<\/p><\/li><li><p>Governance rules that are inconsistent across tools<\/p><\/li><\/ul><p>Databricks addresses these issues by creating a shared data foundation. It combines the flexibility of a data lake with the reliability and performance expected from a data warehouse. This architecture is often called a lakehouse. In plain language, a lakehouse allows organizations to store large volumes of different data types while still applying structure, quality controls, and governance. That makes it suitable for both traditional analytics and AI workloads.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-db0b0fb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"db0b0fb\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b2ee1f0\" data-id=\"b2ee1f0\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ee801eb elementor-widget elementor-widget-text-editor\" data-id=\"ee801eb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">Who is Databricks for?<\/h2><p>Databricks is mainly intended for organizations that need to work with data at scale. It is especially relevant when data is strategic to the business and when multiple teams need to collaborate on the same data foundation.<\/p><p>Typical users include:<\/p><ul><li><p><strong>Data engineers.<\/strong> They use Databricks to build pipelines, ingest data, transform datasets, automate workflows, and prepare reliable data products.<\/p><\/li><li><p><strong>Data analysts.<\/strong> They use Databricks for SQL analytics, dashboards, and reporting on governed datasets.<\/p><\/li><li><p><strong>Data scientists.<\/strong> They use Databricks to explore data, train models, run experiments, and collaborate with engineering teams.<\/p><\/li><li><p><strong>Machine learning and AI engineers.<\/strong> They use Databricks to build, evaluate, deploy, and monitor models, AI applications, and agents.<\/p><\/li><li><p><strong>Platform and governance teams.<\/strong> They use Databricks to manage access, lineage, quality, cost controls, and workspace standards.<\/p><\/li><li><p><strong>Business leaders.<\/strong> They do not usually work in Databricks every day; however, they benefit from faster analytics, better AI readiness, and more reliable decision-making.<\/p><\/li><\/ul><p>Databricks is less relevant when an organization only needs small-scale reporting from a single application or when a simple spreadsheet-based process is still sufficient. It becomes more valuable when data volume, complexity, governance needs, or AI ambitions increase.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d0d305 elementor-widget elementor-widget-text-editor\" data-id=\"2d0d305\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">What does Databricks do exactly?<\/h2><p>Databricks supports several core functions.<\/p><h5><strong>1. Data engineering<\/strong><\/h5><p>Data engineering is the process of moving, cleaning, and preparing data. Databricks helps teams build pipelines that can handle batch and streaming data. For example, an organization can ingest transaction data every hour, combine it with customer data, validate the results, and publish a clean dataset for reporting. For decision-makers, this means fewer manual exports, fewer fragile scripts, and more repeatable data processes.<\/p><h5><strong>2. Data warehousing and BI<\/strong><\/h5><p>Databricks can also be used for SQL analytics and business intelligence. Teams can query curated datasets, build dashboards, and serve reporting tools with governed data. This is relevant for organizations that want one platform for both data engineering and reporting, rather than moving data through multiple separate systems.<\/p><h5><strong>3. Machine learning and AI<\/strong><\/h5><p>Databricks supports the full machine learning lifecycle: experimentation, feature preparation, model training, deployment, and monitoring. It also supports generative AI use cases, such as retrieval-augmented generation, AI assistants, and domain-specific agents. The key point for decision-makers is that AI quality depends heavily on data quality. Databricks helps connect AI development to governed enterprise data instead of isolated prototypes.<\/p><h5><strong>4. Governance and security<\/strong><\/h5><p>Databricks includes governance capabilities that help teams manage access, metadata, lineage, and policies across data and AI assets. This is critical when data includes customer information, financial data, operational data, or sensitive business logic.<\/p><p>A strong governance layer helps organizations answer questions such as:<\/p><ul><li><p>Who can access this dataset?<\/p><\/li><li><p>Where did this number come from?<\/p><\/li><li><p>Which dashboards use this table?<\/p><\/li><li><p>Which models depend on this data?<\/p><\/li><li><p>Is this data approved for AI use?<\/p><\/li><\/ul><h5><strong>5. Real-time and streaming use cases<\/strong><\/h5><p>Some organizations need to act on data quickly. Examples include fraud signals, sensor monitoring, logistics events, customer behavior, security telemetry, or operational alerts. Databricks supports streaming data workflows, which means teams can process data as it arrives instead of waiting for a daily batch.<\/p><h5><strong>6. Data and AI applications<\/strong><\/h5><p>Databricks is increasingly used to build applications that work directly with enterprise data. These may include internal tools, AI assistants, planning applications, or operational dashboards. This is important because many organizations want AI to move beyond experiments. They need applications that are secure, governed, and connected to current business data.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ebbe18e elementor-widget elementor-widget-text-editor\" data-id=\"ebbe18e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">Databricks on Azure, AWS, and GCP<\/h2><p>Databricks runs natively on three major clouds, with the same core platform, Spark, Delta Lake, notebooks, MLflow, SQL analytics, underneath each one. What differs between them is the integration layer: how Databricks connects to each cloud&#8217;s identity system, storage, networking, and native services. Most organizations choose based on where they already run infrastructure, not on which cloud runs Databricks best.<\/p><h5><strong>Databricks on Azure<\/strong><\/h5><p>Many Dutch organizations use Databricks through Azure Databricks. Azure Databricks is the Databricks platform integrated with Microsoft Azure services, identity, networking, and cloud infrastructure. It holds first-party status on Azure, meaning support cases can route through Microsoft&#8217;s own enterprise support channels and billing can run through an existing Azure Enterprise Agreement. For organizations already committed to Azure, especially ones also running Power BI or Microsoft Purview, this can mean one bill, one identity system, and one support relationship instead of three.<\/p><h5><strong>Databricks on AWS<\/strong><\/h5><p>AWS was the first cloud Databricks launched on, and it remains the most established of the three deployments. Databricks on AWS integrates with Amazon S3 for storage, AWS IAM for identity and access management, and AWS PrivateLink for private network connectivity, alongside complementary services like Redshift and AWS Glue. Organizations that already run their core infrastructure on AWS often choose Databricks on AWS for exactly that reason: it slots directly into an environment they&#8217;ve already built, rather than asking them to adopt a second cloud&#8217;s identity and storage model.<\/p><h5><strong>Databricks on GCP<\/strong><\/h5><p>Databricks on Google Cloud is the newest of the three deployments, running on Google Compute Engine and integrating with Google Cloud Identity, Google Cloud Storage, and BigQuery. The standout feature here is BigQuery federation: teams can query Delta Lake tables in Databricks alongside BigQuery datasets without moving data between the two. That matters most for organizations already invested in BigQuery for SQL analytics that want to add Databricks for Spark workloads and ML training without replacing what&#8217;s already working. Vertex AI integration connects models trained in Databricks to Google&#8217;s own inference infrastructure.<\/p><h5><strong>Choosing between them<\/strong><\/h5><p>For organizations in Amsterdam and the wider Netherlands, or anywhere else, the decision is often less about the brand of cloud and more about the operating model. Questions to ask include:<\/p><ul><li><p>Which cloud platform do we already use?<\/p><\/li><li><p>Where is our data stored?<\/p><\/li><li><p>What are our security and compliance requirements?<\/p><\/li><li><p>Which teams need access?<\/p><\/li><li><p>Do we have the skills to run Databricks effectively?<\/p><\/li><li><p>Which first use case can prove value quickly?<\/p><\/li><\/ul><p>RevoData helps organizations answer those questions and translate them into an implementation path, on whichever of the three clouds that turns out to be. The skills question deserves a direct answer: if the goal is running Databricks well without building that operational capability in-house first, Managed Databricks lets RevoData run the platform on your behalf, handling day-to-day operations, security, and reliability while your team focuses on using the platform rather than maintaining it.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab4e44e elementor-widget elementor-widget-text-editor\" data-id=\"ab4e44e\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">Databricks compared with alternatives<\/h2><p>Choosing a cloud is one part of the decision. The other part is knowing how Databricks itself stacks up against other platforms you might be weighing instead. Decision-makers often compare Databricks with other analytics platforms, cloud-native data warehouses, or integrated BI and data environments. The right choice depends on the organization&#8217;s data maturity, cloud strategy, and use cases.<\/p><p>A warehouse-first platform can be a strong fit for structured reporting, SQL workloads, and dashboarding. An integrated analytics suite can be attractive for organizations that want tight alignment with office productivity, BI, and low-code tooling. A specialized machine learning platform may fit teams focused on model development.<\/p><p>Databricks is often strongest when the organization needs one open foundation for data engineering, analytics, AI, and machine learning at scale. It is especially relevant when data is stored in a lakehouse architecture, when teams work with large or mixed data types, or when AI initiatives need governed access to enterprise data.<\/p><p>A useful decision rule:<\/p><ul><li><p>Choose a BI-first tool when the main need is business reporting<\/p><\/li><li><p>Choose a warehouse-first approach when the main need is structured analytics<\/p><\/li><li><p>Choose Databricks when the need spans data engineering, analytics, governance, machine learning, and AI<\/p><\/li><li><p>Choose a combined architecture when different teams need different interfaces on top of the same governed data foundation<\/p><\/li><\/ul><p>RevoData helps organizations avoid tool-led decisions. The better starting point is the business outcome, the data landscape, and the operating model.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-763f522 elementor-widget elementor-widget-text-editor\" data-id=\"763f522\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">Databricks certification and skills<\/h2><p data-pm-slice=\"1 1 []\">Choosing Databricks is one decision. Having the skills to actually run it well is a separate one, and that&#8217;s what certification is meant to support. Databricks certification helps professionals prove practical knowledge of the platform. Certifications are available for roles such as data engineer, machine learning practitioner, and solution architect.<\/p><p>For organizations, certification matters because Databricks projects require more than access to the platform. Teams need to understand architecture, governance, cost management, pipeline design, data modeling, security, and deployment patterns.<\/p><p>RevoData&#8217;s strength is that its consultants are Databricks-certified, and RevoData itself is a Gold-certified Databricks consulting partner. This means common architecture mistakes and expensive rework get caught during design instead of after go-live, which helps clients move faster from assessment to delivery. RevoData also has a strong focus on continuous learning and has one of the highest numbers of Databricks Champions in EMEA.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-313f109 elementor-widget elementor-widget-text-editor\" data-id=\"313f109\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">Databricks Community Edition and Free Edition<\/h2><p>Certified expertise has to start somewhere, and for a lot of people, that starting point is trying Databricks hands-on before committing to anything larger. For years, Databricks Community Edition was a common way for individuals to try Databricks. That has now been replaced by Databricks Free Edition.<\/p><p>For decision-makers, the important point is that a free environment is useful for learning the concepts, but it isn&#8217;t a substitute for a properly scoped implementation, one with real governance, representative data, cost visibility, and clear success criteria behind it. A demo environment can show that the platform works. It can&#8217;t tell you whether it solves your actual business problem.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f30ec95 elementor-widget elementor-widget-text-editor\" data-id=\"f30ec95\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">Current valuation and market position<\/h2><p>Once you&#8217;ve tried the platform itself, the next natural question is usually about the company behind it: is this a safe long-term bet? Databricks is one of the most visible private companies in the data and AI market. Its valuation and revenue growth show that the platform has significant market traction, especially as organizations invest in AI and governed data foundations.<\/p><p>For buyers, valuation should not be the main decision factor. It does, however, indicate that Databricks is not a niche tool. It is a major enterprise platform with a large ecosystem, strong investor backing, and continued product investment. What matters more than either of those signals is fit: whether Databricks lines up with your use cases, existing architecture, skills, governance requirements, and expected business value.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-71817f8 elementor-widget elementor-widget-text-editor\" data-id=\"71817f8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 3 []\">How to start with Databricks: a PoC path via RevoData<\/h2><p>A successful Databricks proof of concept should be focused, not trying to rebuild the entire data platform at once. RevoData typically recommends a practical PoC path for those seriously considering the platform:<\/p><ol><li><p><strong>Select a high-value use case.<\/strong> Choose a use case with measurable business value. Examples include faster reporting, improved forecasting, customer segmentation, operational analytics, geospatial analysis, AI readiness, or automated data quality checks.<\/p><\/li><li><p><strong>Assess the current data landscape.<\/strong> Map the relevant source systems, data owners, quality issues, access rules, and existing reporting flows. This prevents the PoC from becoming a technology demo without organizational context.<\/p><\/li><li><p><strong>Define success criteria.<\/strong> Success criteria should be specific. Examples include reducing processing time, improving data freshness, replacing manual steps, improving governance visibility, or enabling a model to move into production.<\/p><\/li><li><p><strong>Build a minimum viable architecture.<\/strong> Create a focused Databricks setup with the required ingestion, transformation, governance, and consumption layers. Keep the scope narrow enough to deliver evidence quickly.<\/p><\/li><li><p><strong>Evaluate business and technical results.<\/strong> At the end of the PoC, decision-makers should know what worked, what needs improvement, what skills are required, and what a broader rollout would involve.<\/p><\/li><li><p><strong>Create a roadmap.<\/strong> A PoC should lead to a roadmap. That roadmap may include platform standards, data product design, governance rollout, cost management, training, and migration priorities, along with a decision on whether the platform will be run in-house or through Managed Databricks.<\/p><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a927010 elementor-widget elementor-widget-text-editor\" data-id=\"a927010\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">Why work with RevoData?<\/h2><p>Following that process on your own is entirely possible, but the speed and risk profile change considerably with the right partner. RevoData helps organizations turn Databricks from a platform choice into a working capability.<\/p><p>As a Databricks Gold Partner with 100% Databricks-certified consultants, RevoData combines technical expertise with delivery experience. The team helps clients define use cases, design architecture, build production-ready pipelines, set up governance, and transfer knowledge to internal teams. A PoC is part of that broader Databricks consultancy. Once a platform is live, that same team can hand off to Managed Databricks for day-to-day operation, so the people who built the platform aren&#8217;t also the ones stuck maintaining it indefinitely.<\/p><p>This is especially valuable for decision-makers who want to reduce risk. Databricks can deliver significant value, but only when implementation choices match the organization&#8217;s goals, skills, and data maturity.<\/p><p><strong><a href=\"https:\/\/revodata.nl\/databricks-consultancy\/\">Ready to explore Databricks with a focused proof of concept?<\/a> RevoData can help you define the use case, architecture, and success criteria before you scale.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d719f82 elementor-widget elementor-widget-text-editor\" data-id=\"d719f82\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<h2 data-pm-slice=\"1 1 []\">Final thought<\/h2><p>Databricks is best understood as a shared foundation, one platform that engineers, analysts, data scientists, and AI teams all build on, rather than a tool that belongs to just one of them. That&#8217;s what turns enterprise data into analytics and AI at scale: reliable, governed, reusable data products everyone can build from.<\/p><p>For decision-makers, that reframes the real question. It&#8217;s less about whether to buy Databricks and more about which business problem to prove first, and what architecture you&#8217;ll need to scale from there. RevoData helps answer that question, with certified Databricks expertise, practical implementation experience, and a clear path from PoC to production, whether that ends with an internal team running the platform or RevoData doing it through Managed Databricks.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9cdb5bc elementor-widget elementor-widget-spacer\" data-id=\"9cdb5bc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f321960 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f321960\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d0cbc49\" data-id=\"d0cbc49\" data-element_type=\"column\" data-e-type=\"column\" data-settings=\"{&quot;background_background&quot;:&quot;gradient&quot;}\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a6e4d3a elementor-widget elementor-widget-heading\" data-id=\"a6e4d3a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">FAQ's<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9f9d088 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"9f9d088\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5782f90 elementor-widget elementor-widget-toggle\" data-id=\"5782f90\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"toggle.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle\">\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-9171\" class=\"elementor-tab-title\" data-tab=\"1\" role=\"button\" aria-controls=\"elementor-tab-content-9171\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">What is Databricks on Azure?<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-9171\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"1\" role=\"region\" aria-labelledby=\"elementor-tab-title-9171\"><p data-pm-slice=\"1 1 []\">Azure Databricks is Databricks integrated with Microsoft Azure. It allows organizations to use Databricks within an Azure environment, including Azure identity, storage, networking, and security patterns. It also holds first-party status on Azure, so support and billing can run through an existing Azure relationship rather than a separate one.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-9172\" class=\"elementor-tab-title\" data-tab=\"2\" role=\"button\" aria-controls=\"elementor-tab-content-9172\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">What is Databricks on AWS?<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-9172\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"2\" role=\"region\" aria-labelledby=\"elementor-tab-title-9172\"><p data-pm-slice=\"1 1 []\">Databricks on AWS is Databricks integrated with Amazon Web Services, using Amazon S3 for storage, AWS IAM for identity and access management, and AWS&#8217;s own networking and security tools. It&#8217;s the original cloud Databricks launched on, and it&#8217;s often the natural choice for organizations that already run their infrastructure on AWS.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-9173\" class=\"elementor-tab-title\" data-tab=\"3\" role=\"button\" aria-controls=\"elementor-tab-content-9173\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">What is Databricks on GCP? <\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-9173\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"3\" role=\"region\" aria-labelledby=\"elementor-tab-title-9173\"><p data-pm-slice=\"1 1 []\">Databricks on GCP is Databricks integrated with Google Cloud, running on Google Compute Engine and using Google Cloud Identity, Google Cloud Storage, and BigQuery. Its standout capability is BigQuery federation, querying Delta Lake tables in Databricks alongside BigQuery data without moving anything between the two.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-9174\" class=\"elementor-tab-title\" data-tab=\"4\" role=\"button\" aria-controls=\"elementor-tab-content-9174\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">When should you use Databricks? <\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-9174\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"4\" role=\"region\" aria-labelledby=\"elementor-tab-title-9174\"><p data-pm-slice=\"1 1 []\">Use Databricks when your organization needs to process large or complex datasets, build reliable data pipelines, combine analytics with AI, govern data centrally, or support machine learning at scale. It is especially useful when data engineering, analytics, and AI teams need to work on the same foundation.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-9175\" class=\"elementor-tab-title\" data-tab=\"5\" role=\"button\" aria-controls=\"elementor-tab-content-9175\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">How does Databricks work?<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-9175\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"5\" role=\"region\" aria-labelledby=\"elementor-tab-title-9175\"><p data-pm-slice=\"1 1 []\">Databricks runs in the cloud and provides workspaces where teams can ingest data, build pipelines, run SQL queries, train models, manage governance, and serve outputs to dashboards, applications, or AI systems. It uses scalable compute and a lakehouse architecture to support both analytics and AI workloads.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-9176\" class=\"elementor-tab-title\" data-tab=\"6\" role=\"button\" aria-controls=\"elementor-tab-content-9176\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">Is Databricks only for technical teams?<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-9176\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"6\" role=\"region\" aria-labelledby=\"elementor-tab-title-9176\"><p data-pm-slice=\"1 1 []\">No. Technical teams usually build and manage the platform, but business teams benefit from faster reporting, better data quality, and more reliable AI outcomes. Business users may consume Databricks outputs through dashboards, applications, or AI assistants.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-9177\" class=\"elementor-tab-title\" data-tab=\"7\" role=\"button\" aria-controls=\"elementor-tab-content-9177\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">Is Databricks suitable for a proof of concept?<\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-9177\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"7\" role=\"region\" aria-labelledby=\"elementor-tab-title-9177\"><p data-pm-slice=\"1 1 []\">Yes, as long as the PoC is focused. A good PoC should test a real business question, use representative data, and include clear success criteria. RevoData can help design a PoC that gives decision-makers evidence for the next investment step.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<div class=\"elementor-toggle-item\">\n\t\t\t\t\t<div id=\"elementor-tab-title-9178\" class=\"elementor-tab-title\" data-tab=\"8\" role=\"button\" aria-controls=\"elementor-tab-content-9178\" aria-expanded=\"false\">\n\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon elementor-toggle-icon-left\" aria-hidden=\"true\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-closed\"><i class=\"fas fa-caret-right\"><\/i><\/span>\n\t\t\t\t\t\t\t\t<span class=\"elementor-toggle-icon-opened\"><i class=\"elementor-toggle-icon-opened fas fa-caret-up\"><\/i><\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/span>\n\t\t\t\t\t\t\t\t\t\t\t\t<a class=\"elementor-toggle-title\" tabindex=\"0\">Can RevoData manage Databricks for us after implementation? <\/a>\n\t\t\t\t\t<\/div>\n\n\t\t\t\t\t<div id=\"elementor-tab-content-9178\" class=\"elementor-tab-content elementor-clearfix\" data-tab=\"8\" role=\"region\" aria-labelledby=\"elementor-tab-title-9178\"><p data-pm-slice=\"1 1 []\">Yes. Managed Databricks is a dedicated RevoData service that takes over day-to-day platform operation: security patching, cost monitoring, performance, and reliability. It&#8217;s built for teams that want the platform running well without building that operational capability internally first.<\/p><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t\t<script type=\"application\/ld+json\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@type\":\"FAQPage\",\"mainEntity\":[{\"@type\":\"Question\",\"name\":\"What is Databricks on Azure?\",\"acceptedAnswer\":{\"@type\":\"Answer\",\"text\":\"<p data-pm-slice=\\\"1 1 []\\\">Azure Databricks is Databricks integrated with Microsoft Azure. It allows organizations to use Databricks within an Azure environment, including Azure identity, storage, networking, and security patterns. 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It helps organizations collect, prepare, govern, analyze, and use data for reporting, machine learning, AI applications, and operational decision-making. For decision-makers, the simplest explanation is this: Databricks gives data teams one shared environment to work with large amounts of data and turn that data into trusted analytics [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":7283,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[14,21],"tags":[],"class_list":["post-7724","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-it","category-databricks"],"_links":{"self":[{"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/posts\/7724","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/comments?post=7724"}],"version-history":[{"count":7,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/posts\/7724\/revisions"}],"predecessor-version":[{"id":7759,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/posts\/7724\/revisions\/7759"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/media\/7283"}],"wp:attachment":[{"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/media?parent=7724"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/categories?post=7724"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/revodata.nl\/nl\/wp-json\/wp\/v2\/tags?post=7724"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}