We are generating more and more data every day. The main problem is not where to get the right amount and quality of data for our experiments, but how to build a reliable and sufficiently robust data preparation and processing pipeline that will meet our requirements.
The Azure Databricks service can help create this environment and facilitate our data analysis process.
What is Azure Databricks?
Databricks is a popular implementation of the Apache Spark analytics platform. Azure Databricks is a fully managed service, making it a simple and convenient alternative to other similar solutions. It aims to be an easy-to-deploy collaborative framework for Data Scientists, Data Engineers, and Business Analysts to collaborate.
The role of Data Engineer, responsible for preparing and cleaning data, is often performed by Data Scientists who were originally only involved in analyzing already prepared data. A significant part of the work of Data Scientists could also be delegated to Data Engineers, but platforms, tools, and processes, for example, make communication complicated.
Azure Databricks service allows us to share our workbooks with each other, so they are not only used to store and run our scripts, but also as practical tools for communication. The data processing pipeline built on Azure Data Storage technologies and the Azure Databricks service encourages easy collaboration with a unified work environment.
Azure Databricks provides a workbook-based environment. We can write our scripts in Python, Scala, R or even SQL, and our Apache Spark cluster becomes available in workbooks. The workbooks contain the steps of data transformation and specify where to store the generated – now structured, cleaned and prepared – data.
What Data Source does Azure Databricks work from?
Azure Databricks helps us ingest and transform data from multiple data sources.
We can ingest our data from multiple sources. For real-time machine learning projects, we can use Kafka, Event Hub, or IoT data sources. We can also batch process data using Azure Data Factory from data sources such as Azure Blob Storage, Azure Data Lake Storage Gen2, Azure Cosmos DB, or Azure SQL Data Warehouse.
We can feed our data into any Azure storage, and Azure Databricks can be connected to it in minutes. We can work with the Spark cluster and our data in a convenient and modern web interface. Through our tasks and scripts running on the cluster, we can turn heterogeneous and noisy data sources into easily interpretable, unified data sources for machine learning, analytics, or other business purposes.
It is worth noting that Azure Databricks is a fully managed service. We don't have to worry about administering Spark clusters: we have all the necessary support tools at our disposal, such as monitoring, logging, and alerts. At the same time, the service also protects us, as unused resources can be released automatically, so we won't have to account for unnecessary idle time.
Anyone who needs to perform Data Engineer tasks in Azure can acquire the necessary knowledge in the Training 360 Azure Data Solutions Implementation (DP-200) course. You can apply by clicking on the link below!
Resources
- https://docs.microsoft.com/hu-hu/azure/azure-databricks/what-is-azure-databricks
- MSPress: DP-200T01 Implementing an Azure Data Solution