Top reasons to migrate SQL Data Warehouse to Azure Synapse Analytics
Over the past few years, many organizations have switched to a data-driven approach for making business decisions. Much of the impetus for this shift has been the advent of cloud-enabled analytics solutions, which can help organizations harness their data to derive insights.
Thanks to modern data and analytics services, organizations can get clear and consistent feedback from their data, enabling them to identify their most successful business practices and prime opportunities for improvement. A few examples include optimizing marketing spends, streamlining supply chains, and even bolstering security through anomaly detection.
To become more data-driven and maintain a competitive advantage, many organizations are continually updating and leveraging new analytics solutions and technologies. An example of this is the shift from using on-premises SQL-based databases to using SQL data warehouses to ingest and process data from multiple disparate data sources. Organizations can host SQL data warehouses on-premises or in the cloud, with the cloud becoming more and more popular due to its scalability, cost-effectiveness, and ease to integrate the data warehouse with cloud-based analytics solutions.
But what if organizations could simplify their IT environment architecture while simultaneously improving their analytics capabilities? This is the question Microsoft was asking when they created Azure Synapse Analytics.
Azure Synapse Analytics
Azure Synapse Analytics, previously known as Azure SQL Data Warehouse, helps organizations improve their decision making by storing and processing all their data while leveraging built-in analytics. Analyzing all their data can help organizations determine their most and least impactful business operations, identify new customer behavior trends, and much more.
It provides an analytics service that focuses on bringing together data warehousing and big data analytics. Synapse offers organizations the freedom to choose between using the serverless or the provisioned resources to reduce time consumption in querying data. It helps break down barriers between the data science and the BI teams, bringing the two worlds together and offering a more efficient and unified experience.
Top features of Azure Synapse Analytics
Apart from providing organizations with actionable insights and integrated services, here are the top features which make migrating SQL DW to Azure Synapse an excellent investment.
1. Integrated data warehousing
Azure Synapse is not just data warehousing. It incorporates integrated data and several other components, such as data visualization, data science, and ETL processes.
2. Write familiar SQL Scripts
While using very little to no code, Data Flow brings familiar SQL scripts right to us with the ability to perform common tasks such as SELECT, sort, filter, etc.
3. Massively Parallel Processing (MPP)
Azure Synapse manages analytical workloads and aggregation and processing of large volumes of data by using Massively Parallel Processing (MPP). MPP databases store each column as an object, in contrast to transactional databases that store rows as objects. Data also gets distributed across nodes that operate in parallel hence facilitating complex, long-running analytical processes.
4. Programming language compatibility
Azure Synapse is the perfect choice for a wide range of analytics and data engineering profiles thanks to its compatibility with a wide range of languages such as Scala, Python, .Net, and much more.
5. Flexible notebook
Synapse allows organizations to write code on the same notebook in any of the following languages – PySpark (Python), Spark (Scala), Spark SQL, and .Net Spark (C#) . This flexibility allows people of different skillsets to collaborate on the same notebook easily.
6. Quick information transfer from Azure Data Lake Storage
Azure Data Lake Storage (ADLS) is the default storage unit for Azure Synapse. It is a single storage platform for ingestion, processing, and visualization of data. Azure Synapse gives organizations various options such as preview data, read data into a notebook, create a dataset, create a data flow, etc., with just a right-click on the file.
7. Run individual ML projects with visualizations
Synapse allows organizations to import their favorite ML packages such as Matplotlib, Numpy, Pandas, etc. Organizations can also create their own ML Models through the Azure Machine Learning Studio. Synapse also gives them the option of linking their own Power BI to create reports and dashboards.
8. Control access across roles
To keep permissions in check, Azure Synapse allows organizations to assign permission based on roles. There are three roles available: Workspace Admin, SQL Admin, and Apache Spark Admin.
At Neal Analytics, we strive to help organizations with the entire end-to-end migration process and jump in at any stage. Being a long-term Microsoft Gold Partner, Neal Analytics holds deep expertise in running migration and modernization projects for Azure and other cloud platforms.
If your organization is ready to take the next step into the future of cloud computing, Contact us.