Learn how to build real-time data ingestion pipelines using Apache Kafka.

Organizations use analytics platforms to build large scale data analytics solutions that generate insights and drive success. Microsoft provides multiple technologies that you can combine to build a large scale data analytics solution.

Learn how to perform data analysis using Azure Databricks. Explore various data ingestion methods and how to integrate data from sources like Azure Data Lake and Azure SQL Database. This module guides you through using collaborative notebooks to perform exploratory data analysis (EDA), so you can visualize, manipulate, and examine data to uncover patterns, anomalies, and correlations.

In most organizations, a data engineer is the primary role responsible for integrating, transforming, and consolidating data from various structured and unstructured data systems into structures that are suitable for building analytics solutions. An Azure data engineer also helps ensure that data pipelines and data stores are high-performing, efficient, organized, and reliable, given a specific set of business requirements and constraints.

Apache Spark is a highly scalable distributed processing solution for big data analytics and transformation. You can leverage its power in Azure Synapse Analytics by using Spark pools.

Build scalable data pipelines using Microsoft Azure tools.