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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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Jobs in using Databricks for but please no
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data engineer
Data Engineer II – QuantumBlack, AI by McKinsey @ quantumblack
GB | 2025-12-28
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Analytics, AI/ML, Data Quality, R, Data Engineering, Agile/Scrum, C, Computer Science, Python, Scala, Java, Docker, Kubernetes, Spark, Cloud Computing, AWS, Azure, GCP, Pandas, NumPy, Matplotlib, DevOps, DataOps, MLOps, PySpark, SQL, Airflow, Databricks, GenAI, Management | ||
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data engineer
Data Engineer @ international-rescue-committee
GB | 2025-12-26
| GBP
48550 - 58800
/ year
The IRC seeks a Data Engineer responsible for ETL processes, data quality, and database management within its Marketing Technology team. The role's differentiator lies in supporting a global...
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Marketing, Management, CRM, Data Engineering, Analytics, Data Quality, ETL/ELT, SQL, Azure, Databricks, VBA, RDBMS, Data Management, Computer Science, dbt, MongoDB, Agile/Scrum, BI, DWH | ||
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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
Data Engineer I - QuantumBlack, AI by McKinsey @ quantumblack
GB | 2025-12-26
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Analytics, AI/ML, Data Quality, R, Data Engineering, Agile/Scrum, C, Computer Science, Python, Pandas, NumPy, Matplotlib, Spark, Cloud Computing, AWS, Azure, GCP, Docker, Kubernetes, DevOps, DataOps, MLOps, PySpark, SQL, Airflow, Databricks, GenAI, Management | ||
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data engineer
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AI/ML, Data Management, Analytics, PySpark, Spark, AWS, Azure, Cloud Computing, GCP, Data Lake, Python, SQL, GitHub, CI/CD, Databricks, Data Lakehouse, dbt, Git, Management, DevOps, Data Engineering, DWH, Data Modelling, Data Quality, Data Analytics, DataViz, Data Science, BI, IaC, Terraform, Tableau, Power BI, Snowflake | ||
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data engineer
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Analytics, BI, SQL Server, ADF, Power BI, DAX, ETL/ELT, Databricks, Data Quality, Data Engineering, SQL, Management, Data Analytics | ||
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data engineer
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Data Engineering, CI/CD, Agile/Scrum, Cloud Computing, Python, Unix, Azure, Parquet, Delta, Docker, Kubernetes, Spark, Databricks, Git, GitHub | ||
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promoted
BigQuery Cost Optimization Guide by Masthead DataThe BigQuery Cost Optimization Guide is a valuable resource that offers a deep dive into understanding and managing the complexities of BigQuery costs. It provides practical guidelines and strategies to help you optimize both storage and compute expenses, ensuring you get the most value out of... |
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data engineer
Data Engineer @ latentview-analytics
GB | 2025-12-22
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Dashboard, Analytics, Data Engineering, Data Analytics, SQL, Data Modelling, Big Data, Snowflake, Databricks, Spark, AWS, GCP, Azure, Tableau, Power BI, Looker, Python, R, API, Git, KPI, ETL/ELT, Data Governance, Qlik, BI, Data Science, Cloud Computing, CI/CD |