Tag Archives: Snowflake

Real time data ingestion using Snowpipe

Data Science, , , , , ,

Pranit Gangurde

Trainee Software Engineer

  • Big Data
  • AI-ML
Pranit is a Big Data, Machine Learning, and AI enthusiast. He has completed the Azure Data Fundamentals and Azure AI Fundamentals certifications. He loves programming, especially in Python, and likes to make code more efficient and reusable. As a Trainee Software Engineer, he is familiar with tools like Databricks, Snowflake PowerBI, and SQL Server. Pranit enjoys weight-training and is on his way to learn calisthenics.

Today’s world is full of data, more appropriately ‘Big Data, with is varied dimensions of Volume, Velocity, and Variety. Of course, there are other dimensions to it as well, such as Value and Veracity, but for the sake of this blog, we can put them aside. There are numerous tools and applications that are hosted […]

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Why should you move to Cloud for Data Warehousing?

Cloud, ,

Abhishek Tanwade

Associate Architect – BI

  • Cloud
  • Big Data
Abhishek Tanwade is an Associate Architect – BI at Nitor Infotech with over 6 years of experience as an AWS Solution Architect (associate certified professional). In his 12 years long career, Abhishek has spearheaded different Data Engineering & Analytics projects thereby gaining experience on various proprietary technologies. He is a distinguished Data Consolidation Patterns expert having knowledge about various Data leveraging technologies. He is a techno-synergist equipped with a strong sense of how Relational Databases work. His experience in visualization tools like Power BI, QlikView, and AWS Quicksight sets him apart. His horizon of expertise expands to enabling Cloud-based ETL services such as AWS glue, Data Pipeline, and Azure Data Factory. Abhishek’s team members vouch that his ever-smiling nature makes for a happier and positive work atmosphere. His personality and great business acumen help them to stay focused and motivates them to aim higher.

So far technology has been taking us for a spin, we have been drowning under that mass called data, if that is not enough Big Data keeps pulling us 5 ways (volume, velocity, variety, veracity, and value). Not painting a great picture am I, but then a host of Data engineers, Data Scientists, and infrastructure […]

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