Scalable Data Engineering With Azure Databricks

After the introduction of Azure Databricks service, lot of enterprise organizations started using Azure Databricks to build data & analytics solutions on Azure.

In this article, we are going to discuss about challenges faced by the organizations as the data increases and how we can use azure databricks to create scalable data engineering solution.

Data Volume Challenges

As the data volume increase including the variety of data and different data velocities, organizations need to adapt the modern data engineering solutions. As we all know now a days, data is the new oil. Considering the increased volume of data, building a solid foundations for the digital transformation uncover and harness the value out of the data to meet the business requirements by making sure that data is available rapidly and different teams should be able to access it efficiently to create & bring business insights.

Major challenges faced by the enterprise organizations are as follows :

Variety of Data
Data solutions should be able to handle variety of data like structured, semi-structured and unstructured data. Variety of data should be available easily to build the data analytics solution.
Scalability of Data
Modern data solutions should be able to cater scalable data and overcome the limit of traditional data warehousing solutions.
Business Insights from the Data
When cloud data solutions are designed efficiently, we can create innovative data solutions to bring the valuable insights from the data and build strong data analytics solution.

Scalable Data Engineering with Azure Databricks

Major benefits of using Azure Databricks for the data engineering workload is to able to integrate multiple data sources to pull the data using the ETL or ELT process.

Extract Data Sources
Azure Databricks is built on the top of spark which is a distributed processing platform to combine & integrate multiple data sources using the distributed file system. When deployed on-prem, data is read from the HDFS.

Azure Databricks has in-built support to connect to Azure Storage services like Azure Blob Storage, Azure Data Lake Store Gen2, Azure SQL DB etc.
Data Transformation
One of the major capabilities delivered by Azure Databricks is to transform the data at scale. Databricks has various API capabilities available like Python, Scala, Java & SQL. Writing a data transformation with any of these language is similar to the writing the SQL statement.

Spark also has extended capabilities to support the handling of the streaming data. So, data can also be ingested in the near-real time and transformation can be done on the fly.
Load the Data
Once the data transformation is done, it is ready to be consumed by the business users for the queries & data scientists to use it to build the machine learning models. Data scientists & analysts can query the data stored in the data lake using the common SQL code. Tables in the Azure Databricks can also be accessed in various formats like CSV,JSON & parquet. Databricks Delta is an extension to the parquet which provides the layer of data management to perform optimizations.

In summary, Azure Databricks is a unique offering which provides scalable, simplified & unified data analytics solutions on Azure. It allows developers to write programs in various languages & has also various in-built APIs.