<img src="https://secure.leadforensics.com/133892.png" alt="" style="display:none;">

Where are you on your data journey?

Several companies mistakenly think they are much higher up on the curve until they eventually realize, "Oh, we're not quite as far as we thought we were." To determine what will generate the maximum value for your business, the first step is to determine the level of difficulty you are prepared to undertake.

Power BI is a key stepping stone in this difficulty level-gauging exercise. It can help you maximize the value of your data and answers questions like ‘Why did this happen?’ and ‘How did this happen?’ Both are reactive scenarios. How about a proactive one where you make certain things happen?

The demand for meaningful insights capable of driving innovation is increasing, resulting in:

  • The data warehouse evolving continuously
  • The coming together of Power BI and Analytics becoming a necessity

Enter Azure Synapse, a limitless analytics service that brings together enterprise data warehousing and Big Data analytics.

Azure Synapse Analytics explained

An advanced (and limitless) analytics version of Azure SQL Data Warehouse, Azure Synapse brings the two worlds of enterprise data warehousing and Big Data analytics together to enable you to ingest, combine, process and transform data.

Azure Synapse comes integrated with an identical data integration engine that you experience in the Azure Data Factory. It allows you to:

  • Create rich at-scale extract-transform-load (ETL) pipelines without leaving Azure Synapse Analytics
  • Ingest data from over 90 data sources
  • Carry out pipeline logical grouping of activity code(s) for ETL with already built-in dataflow activities
  • Orchestrate notebooks, spark jobs, stored procedures as well as SQL scripts
  • Create data-driven workflows for orchestrating and automating data movement and transformation

You can further process and transform data into actionable insights using data transformation activities in the Azure Data Factory with built-in flows such as:

  • Mapping data flows – These are data transformations that are visually designed in the Azure Data Factory. Data engineers are not required to write even a single line of code to build data transformation logic. The obtained data flows are implemented as activities within Azure Data Factory pipelines that use scaled-out Apache Spark clusters. Using existing Azure Data Factory scheduling, control, flow and monitoring capabilities, data flow activities can be operationalized.
  • Wrangling data flows - For accurate analysis of complex and growing data, organizations need to do data wrangling and preparation. Data preparation is necessary to reduce the time to value and use that data in numerous business processes. Wrangling data flows are especially helpful for data engineers and citizen data integrators.

How can Azure Synapse Analytics help you?

Both Power BI and Azure Machine Learning natively integrate with Azure Synapse Analytics to give your developers a platform that is built for collaboration and driving greater business value. This enables frictionless collaboration between business analysts using Power BI, data engineers using Azure Synapse, and data scientists using Azure Machine Learning. With Azure, you can provide insights across your entire organization in real-time.

How can Azure Synapse Analytics help you?

Enhance business value with Azure Synapse Analytics
(Source - Microsoft)

Traditionally, analyzing and extracting insights from such massive amounts of data took hours, if not days. It was not uncommon for organizations to have petabytes of data across the organization. The new aggregation feature in Power BI—in sharp contrast—caches data at the aggregated level, enabling instant response times even on petabyte-scale data sets with trillions of rows. The integration of Power BI and Azure Synapse is more than just reporting and dashboards. Instead, it enables:

  • Interactive data exploration, self-service analytics, predictive analytics, machine learning and prescriptive analytics
  • Exciting new possibilities for bridging the gap between massive volumes of unstructured and structured data and actionable insights
  • A familiar Power BI workspace that allows the rapid creation of feature-rich visual reports and a performant, scalable data warehouse
  • Access to published reports in your Azure Synapse workspace
  • Updation and publication of reports in real-time that get reflected in the Power BI workspace

Additional advantages

Apart from connecting with Azure for Machine Learning and with Power BI for data visualization and reporting, Azure Synapse connects natively to several other Microsoft and Azure data services including:

  • Azure Data Lake for data lake storage
  • Azure Blob Storage for storage container
  • Azure Active Directory for authentication
  •  Azure Databricks for analysis via Spark or Azure Data Warehouse

A solution to keep on the radar

Azure Synapse Analytics can break down all silos related to data insights and analytics by leveraging the capability and scale of Microsoft Azure and deriving meaningful insights by integrating with Power BI.

At Columbus, we have been providing data warehousing and BI solutions to our customers successfully for years now. Our experts will be happy to chart a plan to help you with your data migration and cloud integration needs. Please write to us at us-marketing@columbusglobal.com.


Discuss this post

Recommended posts

A 2021 McKinsey report found that the use of artificial intelligence for marketing efforts, business processes and product/service development has become widespread. In fact, 56% of respondents said their business uses AI for at least one function, which is a 6 percentage-point increase from last year. 
A recent industry report describes artificial intelligence (AI) as ‘a self-running engine for growth in healthcare with immense power to unleash improvements in cost, quality and access. Growth in the AI health market is expected to reach $6.6 billion by 2021— a compound annual growth rate of 40%. In just the next five years, the AI health market will grow more than 10X2.’
Did you know that the Microsoft Power BI solution was originally designed for non-technical users? The intent was to give business professionals across the board access to critical data; a privilege once reserved for only IT and data personnel.
While most organizations understand the importance of data, far fewer have figured out how to successfully become a data-driven company. It’s enticing to focus on the “bells and whistles” of machine learning and artificial intelligence algorithms that can take raw data and create actionable insights. However, before you can take advantage of advanced analytics tools, there are other stops along the way, from operational reporting to intelligent learning. Digital transformation is dependent on adoption. But adoption and proficiency of new technologies can be disruptive to an organization. Mapping a data journey provides awareness and understanding of where your organization is to ultimately get where you want to go, with enablement and adoption of the technology throughout. Without the clarity provided by a data journey, your organization won’t be positioned to successfully deploy the latest technology.
Pricewaterhouse Coopers forecasts that Artificial Intelligence (AI) could contribute up to $15.7 trillion to the global economy by 2030, of which $6.6 trillion is likely to come from increased productivity and $9.1 trillion from consumption-side effects.
right-arrow share search phone phone-filled menu filter envelope envelope-filled close checkmark caret-down arrow-up arrow-right arrow-left arrow-down