Journey of The Data Warrior

Semi-retired Snowflake data cloud and data vault evangelist

A semi-retired promoter for the Snowflake data cloud and data vault, Kent Graziano is known affectionately as "The Data Warrior." He has received recognition as an industry thought leader, author, and speaker. The concepts shared by Kent Graziano can produce excellent value for businesses that are data-driven. His partnership with Lyftrondata, a modern data fabric platform, has generated a lot of attention and is anticipated to provide game-changing innovation in the data fabric environment (data pipeline, data virtualization, data catalog, and data governance),

Key Highlights

  • With the strength of Lyftrondata's Snowflake ELT engine, how innovation in data pipeline and data virtualization is being taken to a new level.
  • Who is the "Data Hero" in reality? What are his duties, obligations, and nemesis?
  • The World Wide Data Vault Consortium's potential and the data vault game.
  • How agility, accuracy, and security are the Data Fabric's three pillars of sustainability.
  • How does Lyftrondata assist businesses in their quest for data modernization?


A set of data integration, processing, and analysis that are interlinked in series, and the output of the previous element is the input of the next element known as the Data Pipeline.

Data Virtualization is a popular approach to data management used for manipulating and retrieving data without much technical insight about the source, location, etc.

Data Catalog is an organized form of data asset which uses metadata for collecting, organizing, enriching, and accessing the data to the professionals for providing support to the data governance and discovery.

Data Governance is the process of managing people, strategy, processes, policies, standards, security, socialization, communication, metrics/KPIs and technologies, etc. concerning data. It is used at micro as well as macro levels for ensuring data is trustworthy and consistent and to avoid any misuse.

Data Fabric is an architectural design that simplifies complex data by providing end-to-end integration. It follows metadata and provides continuous capabilities of choosing end-point spanning hybrid multi-cloud environment.

Data Hero is a product of Snowflake which offers a community-like data ecosystem on-premises, clouds, streams, and the edge of data. It provides the environment with Snowflake guidance for content creation, data handling, training, expert direction, etc.

WWDVC is the short form of the World Wide Data Vault Consortium. It is the world's best program that discusses Data Vault for data lake, big data, data science, and data hub.

Kent Graziano is the Chief Technical Evangelist for Snowflake Inc ( and the owner of Data Warrior LLC. He is a recognized industry expert who recently attended the Coffee With Data podcast and provided his valuable insights.

Extract, transform, and load is the full form of ETL. It is a process of extracting databases, files, SaaS applications, workflows, and the transforming Staging Area and loading them into the Data Warehouse and lastly commits analysis.

Building Data Pipeline is the first step in setting up the dependencies necessary to compile and deploy the project. It is used for the dependencies to set up environments for the tracking API that sends events to the pipeline, and the data pipeline that processes events.

3 Ways to Build An ETL Process with Building an ETL Pipeline with Batch Processing.

  • ETL pipeline, you process data in batches from source databases to a data warehouse.
  • Building an ETL Pipeline with Stream Processing.
  • Building a Pipeline without ETL Using an Automated Cloud Data Warehouse.

Data Pipeline for Snowflake is worked as the Modern Data Engineering for moving data into Data Warehouses or Data Lakes.

The steps for Designing a Data Pipeline are as follows:

  • Step 1: Determine the goal
  • Step 2: Select the data source
  • Step 3: Determine the strategy for data ingestion.
  • Step 4: Design the Data Processing Plan
  • Step 5: Set up storage for the output of the pipeline.
  • Step 6: Plan the data workflow

The points to consider when building a pipeline for Snowflake are as follows:

  • Perpetual and extensible data processing.
  • The elasticity and skill of the cloud.
  • Unique and dominant resources for data processing.
  • Democratized data access and self-service supervision.
  • High availability and crisis rescue.

Data Vault is a model that facilitates long-term data storage of historical data in multiple operating systems. It is used for managing business analytics to assist in business intelligence, data warehousing, analytics, and data science requirements.

Lyftrondata's an agile, modern data delivery platform that enables bulk data loading of selected data from the legacy Data Warehouse to the new database. It is a cost-effective solution, which provides reliable and effective data delivery.

Lyftrondata offers agility, scalability, and efficiency through its Data Virtualization. It manages, integrates, and enables data for access in real-time regardless of which source it belongs to. For more information, you can directly check out Lyftrondata's Data Virtualization by clicking here.

About Coffee With Data

Listen, engage and learn data with industry thought leaders.


The Coffee With Data podcast series is for data and business leaders to learn how they leverage the cloud to unite, share, and analyze data to drive business growth, fuel innovation, and disrupt their industries. The data topic covered shall empower our future guests and our engaging audiences – Data Governance, Data Management, Data Science, Data Quality, Data Strategy, Data Architecture, Data Analytics, Machine Learning, Artificial Intelligence, Data Security and Privacy, Master Data Management are to name a few.

Host | George Firican & Diana Firican
Founders at LightsOnData
Speaker | Kent Graziano
Strategic Advisor at Lyftron Data Inc.

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