Today, all businesses are data businesses — thus, every organization needs a data strategy at its heart. Data is the lifeblood of any business and it is widely heralded as the new oil. Like water, data needs to be accessible, it needs to be clear and it is needed to survive for every organization. This article talks about the tools Azure has provided for Data Warehousing — Azure Synapse Analytics. Data Warehouses and Data Lakes are vital parts of business intelligence and analytics and with these proper tools of the trade, the article explains, decision-making hasn’t ever been easier.
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Data Science refers to the amalgamation of interdisciplinary domains such as mathematics, statistics, programming, and more to use algorithms, processes, and scientific methods to find insights and extract knowledge from data.
Data Lakes are often used by Data Scientists. Synonymous to its name, Data Lake can be understood just like a repository which is mainly used for storage of a huge amount of raw structured and unstructured data for its possible usage at some point in time. Unlike Data Warehouses that stores data in files, the data lake stores data in a flat architecture.
Business Analytics is the process of analyzing data using various statistical approaches and methods in order to analyze historical data which can provide insights to help make strategic decisions.
Data Warehouse can be understood as a warehouse of data that consists of large volumes of data that are used to support organizations to make decisions. Data Warehouses assist organizations with business intelligence and analytics that help in decision-making. Data Warehouse is different from Database, Data Lake, and Data Mart. Data Warehousing is enabled by Azure Synapse which can fetch data from an On-premises network or Cloud into Storage blob to perform the required operations and analysis on the data.
Is the Data Warehouse still relevant?
- Contrary to popular belief, Data Warehouse is relevant today even with data lake and big data in existence. Data Warehouse is not just used to store data but moreover for analytics, to drive innovation forward and encourage collaboration and data sharing. Not every organization can work with Data Lakes or shift to it and even with big data in the scene, a huge portion of organizations do not need such a degree of scalability and size.
On-Premises VS Cloud
The major benefit of switching from On-Premises to Cloud is how we can scale our resources. Learn about this from the previous article.
Data Warehouse in the Cloud
Data Warehouse is the central repository for data that are integrated from one or more distributed sources. Henceforth, the data is moved into the warehouse periodically by extracting for the sources. These data can be easily cleaned, formatted, summarized, reorganized, and validated. Also, the data can be stored with few details. In both cases, data warehouses act as the permanent storage of data for business intelligence, analytics, and reporting.
Data Ingestion is a method to move data from one or more resources to a specific location for storage and future analysis. It is the process by which data are stored in the data warehouses.
In Microsoft Azure, we have the following architecture for Data Warehousing,
Enterprise BI in Azure with Azure Synapse Analytics
For moving data from the On-Premises SQL Server database to Azure Synapse, this end-to-end process supports implementing the extract, load, and transform (ELT) pipeline.
Automated enterprise BI with Azure Synapse and Azure Data Factory
The ELT pipeline is automated using Azure Data Factoring with incremental loading.
Azure Data Factory
Azure Data Factory is a platform provided by Microsoft for data integration performed using the serverless architecture to inject, prepare and transform data with scalability. It is a solution for ELT and data integration service allowing the creation of workflows that are data-driven to orchestrate movements of data and scalable transforming of data.
Azure Data Bricks
Azure Data Bricks enables data analytics to be performed in Azure cloud platforms. The data whether structured or unstructured are ingested through Azure Data Factory in batches or streamed using IoT Hub, Event Hub, and Apache Kafka in Azure. It is basically a cloud-based engineering tool in Azure that is used to process and transform a huge volume of data and explore using machine learning models.
Azure Data Lake Storage
Azure Data Lake Storage is Microsoft’s way to provide storage for Data Lake. Also known as ADLS, it is designed to run a massive-scale analytic system that requires humongous capabilities of computing in order to analyze and process large amounts of data. Azure Data Lake Storage is an elastic, scalable secure file system that supports the HDFS semantics and is used with Apache Hadoop Ecosystem.
Azure Machine Learning
Azure Machine Learning provides a platform to build and deploy enterprise-grade machine learning models with numerous features such as Autoscaling compute, Drag-and-drop machine learning, automated machine learning, cost management, and more.
Power BI enables users with business intelligence capabilities and interactive visualizations to produce dashboards and reports.
Big Data, as the name suggests, can be understood as the collection of large volume sof data that can be both raw or structured and are continuously growing in size with time. The size of the data is so massive that the traditional tools don’t support us to operate on this data.
Azure Synapse Analytics
Azure Synapse is a limitless enterprise analytics service that enables us to get insight from data analytics and data warehousing. Using dedicated resources or serverless architecture, data can be queried and provides scalability as per the increase in the size of the data.
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