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Data Engineering in Microsoft Fabric

What is Data Engineering?

Data Engineering

Data Engineering is a Microsoft Fabric service that enables large-scale data engineering and analysis. It provides comprehensive tools for data processing, collection and transformation, allowing for easy integration of different data sources.

Data integration

Lakehouse Data Engineering combines the best of a data lake and data warehouse, eliminating the hassle of ingesting, transforming and sharing organizational data, all in an open format.

Versatility of use

Data Engineering performs well in projects related to data analysis, business intelligence, or the development of advanced data-driven applications. It provides scalability and flexibility to accommodate a variety of data processing needs and solutions.

Scope of Possibilities

How can Data Engineering in Microsoft Fabric service help your business?

Data Engineering streamlines work with corporate data. Instead of wasting time connecting more products, worrying about running and managing infrastructure, and combining different data sources, data engineers can focus on the key tasks at hand.

  • Transforming large data sets
  • Advanced Big Data analytics
  • Building the OneLake architecture
  • Facilitated data collaboration
  • Creation of analytical reports
  • Integration with other MF services
Synapse Data Engineering w usłudze Microsoft Fabric
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AI

Copilot in Data Engineering

With GPT-based Copilot, data engineers can:

  • Generate Spark and T-SQL queries using natural language,
  • speed up code debugging and data transformation,
  • build data pipelines through simple text commands
  • Identify and fix data transformation errors automatically.

Example: instead of manually writing code to convert sales data – Copilot generates it in seconds based on the command: “Filter data from the last 6 months and calculate the average order value.”

Business value

Benefits of implementing Microsoft Fabric Data Engineering

Reduction in operating costs by up to 30%

With serverless architecture and automatic scaling, organizations eliminate the need to maintain costly test, staging and production environments. In practice, this means up to 30% lower data infrastructure costs (compared to traditional on-premises or IaaS environments) and no charges for unused resources - you only pay for actual processing.

Increase efficiency of analytical teams by 40%

A modern notebook environment (Spark + T-SQL + Python), ready-to-use connectors for more than 200 data sources and integration with OneLake significantly accelerate the work of data & analytics teams. As a result, it is possible to reduce development and maintenance pipeline time by up to 40%,

Reduce reporting time by up to 60%

Through integration with Power BI and Lakehouse's capabilities, data is processed and prepared for analysis in real time, reducing data preparation time from days to hours, automating data update pipelines and schedules, and making business decisions faster.

Faster time-to-market

Reduce deployment time for data-driven projects, such as BI and ML projects can be launched up to 2x faster with Fabric's ready-to-use features and access to data from multiple sources in real time - no manual synchronization required.

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Modern data engineering

Data Engineering: the key to effective data analysis

Data engineering is a key part of the operation of any modern organization. The vast amount of diverse data requires efficient large-scale processing. Data engineers have to meet a number of challenges, such as consolidating data, ensuring information security and democratizing access to meet the diverse needs of users. However, these tasks are not easy to accomplish. Often, data are scattered across multiple sources, making the process of collecting and synchronizing them difficult. They are also sometimes duplicated, leading to problems of inconsistent information. These challenges can block projects, reducing productivity. To avoid them, it is logical to use Data Engineering.

  • Advanced Data Engineering
  • Using artificial intelligence to better handle processes
  • Higher level of work quality
  • Fast and efficient data analysis and reporting
  • One environment for comprehensive data analysis
  • Save time, resources and money

200 +

completed implementations

500 +

satisfied customers

1000 +

developed analyses

10000 +

trained customers
Data Engineering

Data Engineering in Micrsoft Fabric service - why implement?

Big Data processing efficiency at a new level

Microsoft Fabric's Data Engineering solution enables fast and efficient processing of large data sets, using advanced techniques and optimizations that significantly reduce the execution time of complex operations.

Scalability and resource management flexibility

Microsoft Fabric service offers resource management flexibility, enabling dynamic adjustment of compute power and memory according to project needs, optimizing costs and performance.

Secure and reliable data processing

Data Engineering in Microsoft Fabric guarantees a high level of data security and reliability in processing, using advanced access control mechanisms, ensuring full trust in analysis results.

Integration with the Microsoft ecosystem and ease of deployment

Thanks to its full integration with the Microsoft Fabric ecosystem, deploying Data Engineering is extremely simple and seamless, allowing you to quickly and seamlessly set up your work environment and start processing data.

Advanced data transformation and cleansing tools

With the rich set of tools in Data Engineering, advanced data transformation and cleansing operations can be performed, leading to more consistent and complete data in the analysis process.

Support for various data sources and formats

Microsoft Fabric service allows easy data integration from different sources and supports various data formats, enabling organizations to use a full range of data for better and more comprehensive analysis.

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