Microsoft Azure Synapse Analytics

Why Microsoft Azure Synapse Analytics?

The strongest driving force of progress today is information, or rather, the amount of data that is growing exponentially every day. The avalanche of disordered and disparate data that falls on companies every day and settles in databases forces businesses to take a fresh look at data receiving, processing and analytics (modern data estate). Gradually, the understanding comes that the existence of large and medium-sized businesses and their commercial success directly depend on the presence of a culture of working with information (analytics culture) and the maturity of their IT infrastructure (infrastructure maturity).

For a long time, ground data warehouses (DWH) were the only possible solution for collecting, consolidating and aggregating data for further analysis, especially when it came to BIG DATA. However, gradually companies come to understand that the maintenance of terrestrial infrastructure with constantly growing volumes of data leads to a linear increase in the cost of collecting and maintaining DWH and servers. In addition, it has become quite difficult to carry out their analytics, because it also began to require additional capacities and labor resources.

As you know, every year the data of an average company almost doubles, and ground-based DWHs are simply not able to cope with such flows. In principle, they are not designed for such rapid growth, volume and speed, and also do not have time to “respond” to the emergence of a large number of new data types.

In the future, such a situation leads to an information explosion, which will completely “immobilize” the company due to the inability to maintain high-quality accounting and analytics, and as a result, deprive it of an understanding of how and in what direction to develop.

As a response to this danger fundamentally new approaches to working with data as well as new solutions were emerged. The most powerful among them is Azure Synapse Analytics platform by Microsoft, which combines several technologies inside one service — importing data from disparate sources into cloud scalable DWH (Azure Data Lake Storage), creating descriptive and diagnostic dashboards (Power BI), as well as predictive and prescriptive analytics, that is, building machine learning models — AI / ML models (Azure Data Bricks, Azure Cognitive Service), and all this — on a single platform.

Data can be stored in different formats such as structured (Relational DB, Business/Custom Apps), semi-structured (CSV, XML, JSON) and unstructured (AUDIO, VIDEO, TEXT). For each data format, the platform offers various processing tools (R, Python, SQL, etc.) that allow you to organize and aggregate them so that you can build a company’s analytical system and create machine learning models right in the cloud.

Microsoft Azure Synapse Analytics opens up additional possibilities for IoT by simultaneously capturing data from many different devices and instantly displaying it in dashboards (data stream) or placing it in Azure Data Lake Storage for further processing, analytics and building AI / ML models (trend analysis). This, in turn, enriches the company’s analytics and allows you to get new information, and as a result, a vision of new opportunities.

Structure of Microsoft Azure Synapse Analytics

• Azure Blob Storage – a staging store where heterogeneous data is exported from various sources in preparation for loading into Azure Synapse Analytics.
• Azure Data Lake Storage — a data lake around which the entire IT infrastructure of the platform is built. The system is built on Azure Storage Blobs with a hierarchical namespace added to allow storage to be organized in a directory-like fashion.
• Azure Data Factory provides the transformation of the data collected in the previous step into a common homogeneous structure in Azure Synapse by loading the data into tables. To speed up the processing of data in the Azure Synapse Analytics platform, Polybase technology is implemented, which allows you to process large flows of information in parallel and increase processing speed and throughput. The Azure Data Factory service allows you to automatically migrate data to a cloud-based, scalable SQL storage that can contain petabytes of information and is designed to store and analyze large datasets.
• Azure Data Bricks is a software solution for building a predictive analytics system for machine learning models and AI systems for solving key business tasks: pricing, forecasting (sales volumes, demand, labor costs, replenishment of commodity / warehouse stocks, equipment failure), personalization of recommendations, risk assessment and many more other tasks that allow you to automate processes and take your business to a whole new level.
• Azure Cognitive Service is a standard set of pre-trained machine learning models that can be applied to data uploaded to the platform to implement projects related to computer vision, video, text, speech analysis, etc. This service will allow businesses to get additional information and take it into account in decision making.
• Azure Analysis Services contains data models that provide an easy and convenient way to view large amounts of data and analyze it in more detail.
• Azure Active Directory provides authentication for users who will connect to Analysis Services through Power BI.
• Microsoft Power BI Embedded allows you to get a visual representation of data sets through Analysis Services and display it on dashboards (dashboards).

An important point: Azure Synapse Analytics is integrated with all services of the MS Azure platform. This means you can use it with all the other modules in the system without the extra hassle of compatibility.

Azure Synapse Analytics is the best choice if:

• instant data update is required (more than once a day);
• terrestrial DWHs cannot cope with the ever-increasing amount of data — lack of capacity, increasing need for computing resources, equipment failure;
• DWH and analytical systems of the company are used only during working hours or for solving a narrow segment of tasks — then the transition to cloud solutions will save significant funds, since Azure Synapse Analytics allows you to pay only for the time of use and power consumption;
• there is a need to scale the business and organize mobile, multi-level access to information of various departments and branches;
• the company wants to work with data in a single workspace from data import to visualization;
• comprehensive security is important.