{"id":41966,"date":"2023-03-22T07:06:47","date_gmt":"2023-03-22T11:06:47","guid":{"rendered":"https:\/\/centricconsulting.com\/?p=41966"},"modified":"2023-03-21T12:09:24","modified_gmt":"2023-03-21T16:09:24","slug":"improving-your-modern-data-warehousing-with-azure-synapse-analytics","status":"publish","type":"post","link":"https:\/\/centricconsulting.com\/blog\/improving-your-modern-data-warehousing-with-azure-synapse-analytics\/","title":{"rendered":"Improving Your Modern Data Warehousing with Azure Synapse Analytics"},"content":{"rendered":"

We walk through how Azure Synapse Analytics can improve how you manage your modern data warehouse in this blog.<\/h2>\n
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When it comes to data, the future of data analytics and insight lies in cloud data warehouses.<\/p>\n

With a traditional on-premises data warehouse, integrating existing operational data with semi-structured and unstructured \u201cbig data\u201d is a major technical challenge.<\/p>\n

Traditional or enterprise data warehousing solutions simply aren\u2019t scalable enough or cost-effective to support the petabytes of data we generate. The need to mitigate the risks and issues in a traditional data warehouse inspired change that led to the birth of cloud or modern data warehousing.<\/strong><\/p>\n

A modern data warehouse lets you bring together all your data at any scale easily. It allows access to insights through analytical dashboards, operational reports or advanced analytics for all your users. And, it combines all your structured, unstructured, semi-structured and streaming data.<\/p>\n

In this blog, we\u2019ll dive into how a specific tool \u2013 Azure Synapse Analytics<\/a> \u2013 improves your modern data warehousing experience.<\/p>\n

Modern Data Warehousing Before Azure Synapse Analytics<\/h2>\n

When it comes to cloud storage at an enterprise level<\/a>, we all think about a data lake, which is raw storage of all structured and unstructured data.<\/strong> Some companies use Delta Lake<\/a> on top of their data lake, which is like a software layer to implement the atomicity, consistency, isolation and durability (ACID) transactions and many more features to the Data Lake.<\/p>\n

\"Azure<\/a><\/p>\n

The above diagram is a classic example of a modern data warehouse in the Microsoft Azure environment. In Microsoft Azure, we refer to a data lake as Azure Data Lake Storage Gen 2 (ADLS Gen 2).<\/p>\n

Before Azure Synapse launched, we used multiple tools to manage our data warehouse<\/a>. For ingesting and processing the data in the data lake, we used Azure Data Factory (ADF) to transform the data and orchestrate the different activities involved in the extract, load and transform (ELT) process.<\/p>\n

To prepare and transform the data, we used Azure Databricks. To store the processed data, we needed a warehouse so we used Azure SQL Data Warehouse (rebranded as Synapse Analytics in 2020).<\/strong><\/p>\n

And finally, we need to integrate with reporting tools like Power BI to create reports on the facts and dimension tables.<\/p>\n

We needed each of these separate tools to process our data from start to finish.<\/p>\n

How Azure Synapse Analytics Changed the Game<\/h2>\n

Azure Synapse brings all the platforms of a data engineering project like a data lake, ELT\/ETL, a warehouse and reporting under one roof.<\/p>\n

\"Azure<\/a><\/p>\n

Azure Synapse Analytics is a unified platform for data engineering projects where the developers can:<\/strong><\/p>\n

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  1. Interact with the data present in the data lake (ADLS Gen 2).<\/li>\n
  2. Create Linked services to connect with over 90 source systems.<\/li>\n
  3. Create Spark notebooks or copy activity to copy data from the data lake or source systems.<\/li>\n
  4. Access analytical pools like a serverless SQL pool, dedicated SQL pool and Apache Spark pool to process the data.<\/li>\n
  5. Transform the data using SQL scripts, notebooks and data flows (a graphic user interface or GUI).<\/li>\n
  6. Train models with Azure Machine Learning automated ML.<\/li>\n
  7. Orchestrate different tasks in one pipeline and schedule them to run periodically.<\/li>\n
  8. Create and access Power BI reports.<\/li>\n
  9. Monitor all the tasks running in Azure Synapse Analytics<\/li>\n
  10. Manage all the access controls and credentials based on assigned roles.<\/li>\n<\/ol>\n

    Now that you can do all of these tasks within one platform, what does that mean for your day-to-day?<\/p>\n

    Adapting to Azure Synapse Analytics<\/h2>\n

    Moving toward Synapse isn\u2019t without its learning curves. Our journey of exploring Azure Synapse was tremendous and full of experiences where we realized the power of Platform Integration that Azure Synapse offers.<\/strong> Here\u2019s what we learned during our transition. The key features that make Synapse lucrative are:<\/p>\n