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Spark on steroids, with a side of notebooks. Touted as the go-to platform for unified analytics, Databricks brings managed Spark clusters, collaborative notebooks, and seamless integration with cloud storage under one (expensive) roof. If you’re wrangling massive datasets or running complex ML workflows, it’s a powerful choice—especially for distributed computing without sweating over cluster management.
But let’s not ignore the elephant in the room: Databricks is a jack-of-all-trades but master of none. Its notebooks are decent, though hardly revolutionary. Its SQL features lag behind dedicated warehouses like Snowflake, and costs can escalate quickly if you’re careless with cluster sizing or fail to shut things down.
Ideal for hybrid teams juggling data engineering and data science, but for pure analytics or lightweight ETL? Consider simpler, cheaper alternatives. A brilliant tool if your problems fit its paradigm—just don’t expect it to be your one-size-fits-all solution.
Used together with Databricks
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Compare to other data warehousesJobs (this month)
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Jobs in using Databricks for but please no
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data engineer
Product Owner / Data Engineer @ iqvia
ES | 2025-12-28
The role involves managing data engineering and AI product development within a fast-paced environment, with the notable differentiator being the need for deep technical fluency combined with...
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Data Engineering, AI/ML, Analytics, Snowflake, Databricks, Agile/Scrum, Data Quality, ETL/ELT, Data Modelling, API, Cloud Computing, Azure, Management, Computer Science, Data Science, Spark | ||
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data engineer
Senior Data Engineer - Lighthouse @ kpmg-espana
ES | 2025-12-27
Senior Data Engineer at KPMG Lighthouse in Madrid promises cloud-native data work with Snowflake, Databricks and Microsoft Fabric as core platforms. You will design, build and optimize scalable...
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Analytics, Cloud Computing, Snowflake, Databricks, Fabric, Python, SQL, Scala, DataOps, CI/CD, Azure, AWS, GCP, Kubernetes, Docker, API | ||
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promoted
The Fundamentals of Analytics EngineeringThe Fundamentals of Analytics Engineering gives a holistic understanding of the analytics engineering lifecycle by integrating principles from both data analysis and engineering. It's a book that teaches concepts and best practices, not just tools and technologies. |
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data engineer
Databricks Platform Engineer (f/m/d) @ axpo-group
ES | 2025-12-26
Axpo seeks a Databricks Platform Engineer to ensure the secure, scalable operation of its Azure-based data platform, with an emphasis on automation and compliance. The standout feature is their...
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Management, Analytics, Databricks, Azure, Terraform, CI/CD, Datadog, Grafana, GDPR/CCPA, API, Computer Science, Cloud Computing, Azure DevOps, Python, Delta, GitHub, Docker, Linux, Airflow, Dagster, Microsoft | ||
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data engineer
Data Engineer (f/m/d) @ axpo-group
ES | 2025-12-25
Axpo’s Group IT role looks like a straight path for a data engineer who likes stitching Databricks on Azure into a lakehouse while juggling on-prem and cloud sources. You’ll build and scale...
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BI, Databricks, Azure, Cloud Computing, ETL/ELT, Delta, Data Lakehouse, Data Quality, Power BI, Data Governance, Data Engineering, Python, PySpark, SQL, Git, CI/CD, GitHub, Computer Science, Agile/Scrum, Microsoft | ||
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data engineer
Data Engineer Scala Spark @ capgemini
ES | 2025-12-25
Capgemini pitches a Data Engineer role inside a global, multi-sector data practice. Expect to build pipelines from scratch, manage batch and real-time workloads with Apache Spark, and stitch ETLs...
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Spark, Python, Scala, Cloud Computing, Azure, AWS, GCP, Databricks, Synapse, Airflow, AI/ML |
