Explore big data and analytics


Several years ago, Tailwind Traders rolled out a new GPS tracking system for all of its delivery vehicles. The new system provides real-time tracking data to your primary datacenter. Your CTO wants your team to look at several years of tracking data in order to determine trends. For example, an important trend might be a spike in deliveries around the holidays that would require hiring additional staff. Through an in-depth analysis of the tracking data that you've recorded, your CTO seeks to predict when changes are necessary, and then proactively take the necessary steps to appropriately manage spikes.

Data comes in all types of forms and formats. When we talk about big data, we're referring to large volumes of data. In this Tailwind Traders scenario, data is collected from the GPS sensors, which includes location information, data from weather systems, and many other sources that generate large amounts of data. This amount of data becomes increasingly hard to make sense of and to base decisions on. The volumes are so large that traditional forms of processing and analysis are no longer appropriate.

Open-source cluster technologies have been developed, over time, to try to deal with these large datasets. Microsoft Azure supports a broad range of technologies and services to provide big data and analytic solutions, including Azure Synapse Analytics, Azure HDInsight, Azure Databricks, and Azure Data Lake Analytics.

Azure Synapse Analytics

Azure Synapse Analytics (formerly Azure SQL Data Warehouse) is a limitless analytics service that brings together enterprise-data warehousing and big-data analytics. You can query data on your terms by using either serverless or provisioned resources at scale. You have a unified experience to ingest, prepare, manage, and serve data for immediate business intelligence and machine learning needs.

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Icon of Azure HDInsight.

Azure HDInsight

Azure HDInsight is a fully managed, open-source analytics service for enterprises. It's a cloud service that makes it easier, faster, and more cost-effective to process massive amounts of data. You can run popular open-source frameworks and create cluster types such as Apache Spark, Apache Hadoop, Apache Kafka, Apache HBase. HDInsight also supports a broad range of scenarios such as extraction, transformation, and loading (ETL), data warehousing, machine learning, and IoT.

Azure Databricks

Azure Databricks helps you unlock insights from all your data and build artificial intelligence solutions. You can set up your Apache Spark environment in minutes, then autoscale and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.

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Icon of Azure Data Lake Analytics.

Azure Data Lake Analytics

Azure Data Lake Analytics is an on-demand analytics job service that simplifies big data. Instead of deploying, configuring, and tuning hardware, you can write queries to transform your data and extract valuable insights. The analytics service can handle jobs of any scale instantly by setting the dial for how much power you need. You only pay for your job when it's running, making it more cost-effective.