Azure Advanced Analytics Enablement

Uncover hidden insights from disparate data, improve decision-making, optimize processes, enhance customer experiences, and drive innovation with advanced analytics on Azure.

Traditional descriptive analytics can provide valuable insights into past events; however, they have limitations in terms of predicting future outcomes, explaining causality, providing a comprehensive understanding of complex systems, and providing actionable recommendations. To overcome these limitations, organizations may need to leverage more advanced analytics techniques, such as predictive or prescriptive analytics.

Making the shift to a predictive approach using advanced analytics can have a significant impact on various aspects of your business, such as enhancing customer experience, optimizing supply chain operations, developing new business models, and much more. It’s essential to have a comprehensive understanding of the potential of advanced analytics and how to apply it to your unique business challenges. With the right approach, advanced analytics can provide you with the right insights to stay ahead of the curve and drive business success.

Why Embrace Azure Advanced Analytics?

Azure advanced analytics enablement can help organizations make better decisions, increase efficiency, gain a competitive advantage, improve customer experiences, and mitigate risks.

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Azure Advanced Analytics Use Case

Adastra supports your organization through all phases and transfers the required knowledge to support your Azure analytics solution. As a Gold Microsoft Partner, you can tap into our vast expertise and let us help you plan, build, and maintain your move to Azure advanced analytics. In this Azure advanced analytics case Adastra recommends using Azure Synapse Analytics and follows the below Azure cloud data zones:

  • Data stored in a source schema structure with no transformation 
  • Varying formats for data storage (Parquet, SQL, csv, orc, dat, json, XML, …) 
  • Low effort to implement 
  • Accessed by IT Professionals 
  • Data stored in a source schema structure with limited transformation
  • Common format for data storage, i.e., Parquet, SQL
  • Data stored in data lake/delta lake.
  • Low effort to implement
  • Accessed by IT professionals/data analysts/data scientists
  • Data stored in an enterprise model schema with significant integration/transformation
  • Common format for data storage, i.e., Parquet, SQL
  • Data stored in a delta lake/SQL table
  • High effort to implement
  • Accessed by IT professionals/data analysts/data scientists
  • Data Stored in a BI model schema aligned to selfservice analytics, by subject area 
  • Common format for data storage, i.e., SQL (physical), view (logical), tabular (options) 
  • Data stored in a BI model 
  • Medium effort to implement 
  • Accessed by business analysts 

Approach to Azure Advanced Analytics 

Adastra’s approach to Azure advanced analytics involves defining the business problem, gathering and preparing data, selecting an analytics tool, and building, validating, testing and deploying analytics models. Following these steps can help organizations leverage advanced analytics to gain valuable insights and improve their business performance.


Define the Business Problem

Identify the specific business problem or use case you are trying to solve with advanced analytics. This could be anything from improving customer retention to reducing supply chain costs. It is important to prioritize business requirements and processes to build a high-level solution design.


Gather and Prepare Data

Gather the data you will need to analyze, and ensure it is clean and properly formatted. This includes data from various sources such as databases, spreadsheets, and sensors. It is essential to ensure that the data you use is accurate, reliable, and complete.


Establish Technology and Integrations

Choose an appropriate analytics tool to perform the analysis and validate technology choices. Azure offers various analytics tools such as Azure Machine Learning, Azure Databricks, and Azure Synapse Analytics. Depending on the specific business problem, you may need to choose different tools or a combination of tools.


Build Analytics Models

Use the analytics tool to build models that can analyze the data and provide insights. This may involve selecting an appropriate algorithm, setting parameters, and tuning the model to get the best results. Perform platform design reviews. Develop solutions, implement user stories and prepare training materials.


Validate and Test Models

Validate the models by testing them against historical data, comparing them to other models, and conducting experiments to ensure they are accurate and reliable. It’s essential to ensure that the models you build are valid and reliable before moving to the next step.


Deploy and Monitor

Deploy the models in a production environment and monitor their performance. This includes monitoring data inputs and outputs and analyzing the results of the models. It is important to continue monitoring the models and adjust as necessary to ensure their ongoing accuracy and effectiveness.

Frequently Asked Questions

Advanced analytics goes beyond the historical reporting and data aggregation of traditional business intelligence, and uses mathematical, probabilistic, and statistical modeling techniques to enable predictive processing and automated decision-making.

Apache Spark is a parallel processing framework that supports in-memory processing to boost the performance of big data analytic applications. Apache Spark in Azure Synapse Analytics is one of Microsoft’s implementations of Apache Spark in the cloud.

Benefits of Apache Spark in Azure Synapse Analytics:

  • Speed and efficiency
  • Ease of creation
  • Ease of use
  • Preloaded libraries for machine learning, data analysis, visualization
  • Scalability

Bring together all your structured, semi-structured and unstructured data to Azure Data Lake Storage and then use Apache Spark pools to clean and transform your structureless data. You can then query the data in Power BI or take the insights from your Apache Spark pools to Azure Cosmos DB to make it accessible through web and mobile apps.

Data Engineering

Apache Spark includes many language features to support preparation and processing of large volumes of data so that it can be made more valuable and then consumed by other services within Azure Synapse Analytics. Multiple languages are available to choose from based on your current expertise/preference/type of use case. Those include C#, Scala, PySpark, Spark SQL, and R.

Machine Learning

MLlib is a machine learning library built on top of Spark that you can directly use from a Spark pool in Azure Synapse Analytics. Spark pools also include Anaconda, which is a Python distribution which provides a variety of libraries and packages for data science and machine learning use.


Spark in Synapse supports Spark structured streaming; you just need to make sure you run the supported version. Streaming processes are running usually running 24/7 and need to be monitored and restarted if there are any errors or maintenance required.

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