Ssenior data and analytics consultant @ Microsoft
Zeeshan is an experienced professional in the field of data and analytics, with a focus on large-scale data systems and the use of data to drive business decisions and digital transformation. He has a broad range of skills and expertise, including data strategy, big data, data analytics, and solution architecture. He has been working in this field for over 10 years, and has a track record of successfully delivering complex software, big data, and advanced analytics solutions to clients.
As a senior data and analytics consultant, Zeeshan likely works with clients to understand their data needs and goals, and helps them to design and implement solutions that meet those needs. This may involve working with a variety of technologies and platforms, such as Hadoop, Spark, and NoSQL databases, as well as advanced analytics tools like machine learning and statistical modeling.
As a solution architect, Zeeshan is responsible for designing and developing the overall architecture of a data or analytics solution, including both the technical components and the overall business strategy. This may involve working with a team of data engineers, data scientists, and other professionals to design and build the solution, as well as communicating with stakeholders and managing the project through to completion.
Overall, Zeeshan's expertise in data and analytics, combined with his extensive experience in delivering complex solutions, make him a valuable asset to any organization seeking to leverage data and analytics to drive business growth and innovation.
Event Driven Data Ingestion Using Azure Databricks Autoloader
Databricks Auto Loader is designed to solve the problem of getting data into the Databricks platform for processing and analysis. The process of loading data into a data platform like Databricks can be time-consuming and complex, especially when working with large volumes of data or data from multiple sources. The Auto Loader feature simplifies this process by allowing users to specify a set of external data sources and a schedule for data loading, and then automatically loading the data from those sources at the specified intervals. By automating the data loading process, the Auto Loader helps users to save time and effort, and enables them to focus on other tasks such as data processing and analysis. It also helps to ensure that data is loaded consistently and reliably, reducing the risk of errors or data loss. Databricks Auto Loader helps to make it easier for users to work with large volumes of data and to get that data into the platform quickly and efficiently, enabling them to focus on using the data to drive insights and business value.Add to agenda