Data engineering

Data Visualization Specialist

Data engineering and data analytics are interdependent. Data engineers provide the infrastructure and data pipelines needed for data analysts and data scientists to perform their work. Data engineers ensure data is available, clean, and properly structured, while data analysts use that data to generate insights.

In a well-functioning data ecosystem, collaboration and communication between data engineering and analytics teams are crucial for delivering meaningful insights and driving data-driven decisions in an organization.

Business Intelligence

Data engineering focuses on the practical application of data collection and data processing. It involves the following:

  1. Data Collection: Gathering data from various sources, such as databases, logs, APIs, sensors, and external datasets.

  2. Data Integration: Combining data from diverse sources to create a unified and structured dataset. This may involve data cleansing, deduplication, and transformation.

  3. Data Storage: Storing data efficiently, which may include data warehousing, data lakes, or other storage solutions.

  4. Data Transformation: Converting raw data into a format suitable for analysis, often using ETL (Extract, Transform, Load) processes.

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