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Azure Synapse is a limitless analytics platform that brings together Enterprise Data Warehousing and Big Data Analytics. It gives you the freedom to query data on your terms, using either serverless or dedicated resources at scale. This webinar introduces a high-level overview of Synapse Analytics and its capabilities.

Synapse Analytics seamlessly unifies the concepts of Enterprise Data Warehousing and Big Data Analytics. This powerful combination facilitates the management and interpretation of vast amounts of data, providing actionable insights for businesses to strategize and make informed decisions.

One of the standout features of Azure Synapse Analytics is its flexibility. It allows you to query data as per your needs, utilizing either serverless or dedicated resources, all on a massive scale.

So, how can you utilize Azure Synapse Analytics to its fullest potential?

Synapse Pipelines are an integral part of Azure Synapse Analytics. They provide robust ETL (Extract, Transform, Load) capabilities, making data management easier and more efficient.

ETL pipelines are essential for data operations as they automate the process of extracting data from various sources, transforming it into a usable format, and loading it into a data warehouse for analysis.

Azure Synapse Analytics leverages the power of SQL and Spark pools to handle complex data operations, allowing for seamless analytics and data processing.

SQL and Spark pools in Synapse are scalable, reliable, and provide real-time analytics capabilities, making them a powerful tool in your data arsenal.

Register now for our webinar and learn more about the Azure Synapse Analytics.

Audience

Data Professionals

Duration

1 hour

Topics

  • Understanding Synapse Analytics for Data Warehousing and Analytics
  • Understanding about Synapse Pipelines for ETL
  • Pools – SQL and Spark

Vikas Mittal

Independent Consultant and Trainer

Vikas is an experienced Data Engineer and Data Scientist, having more than 20+ years of experience in the field of Data Science, Machine Learning, Deep Learning, Big Data, Data Analytics, Data Warehousing, Reporting, ETL, and Databases.

He has played a significant role in some Data Engineering Use cases (involving Machine Learning) like Fraud Detection, NLP, Social Media Analytics, Voter Perception, Customer Segmentation, etc. He is also an expert on Public Clouds and the required services. He is presently working as an Independent consultant and trainer, a Microsoft Certified Trainer, and a Google Cloud Instructor Contact.

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