How to build data pipelines for Snowflake

Hear industry experts as they shared how data fabric platforms are empowering enterprises to build and automate ETL/ELT data pipelines within minutes for Snowflake to run real-time analytics..


  • Utilizing Snowflake, how to consistently carry out high-performance transformation.
  • Use Lyftrondata’s automatic ETL data pipeline to connect with the database, load data into Snowflake, and make it available in your favorite BI or analytics tool.
  • Provide search functionality on your company data catalog and convert data from any source into a normalized, query-ready relational structure.
  • Augment, cleanse, filter, standardize, and join data easily.
  • Automate extraction and loading of data from a wide collection of on-premises and cloud sources to Snowflake.
  • Increase data modeling speed and go beyond data replication's constraints.


Data modeling in software engineering is the technique of developing a Data Model for an information strategy by assigning specific conventional procedures.

Data democratization management is the ability for information in a digital format to be accessible to the average end-user. The goal of data democratization is to allow non-specialists to be able to gather and analyze data without requiring outside help.

IT infrastructure scaling can be done horizontally or vertically. Vertical scaling involves adding more power and resources to a cloud; horizontal scaling adds more servers. When it comes to scaling, vertical scaling is a better short-term solution, while horizontal scaling is a better long-term solution.

ELT is an abbreviation for “Extract, Load, and Transform”. This clarifies the three arenas of the Modern Data Pipeline. The ELT process is more cost-effective than ETL.

Data Replication means evaluating involves sharing data to ensure consistency between repetitious resources, like software or hardware elements, to enhance dependability, fault-tolerance, or accessibility.

Performance Transformation means Developing and executing transformative processes for practical strategies and managing the delivery.

The process of Loading data to Snowflake is as follows:

  • STEP-1: Use the demo_db Database
  • STEP-2: Create the Contacts Table
  • STEP-3: Populate the Table with Records.
  • STEP-4: Create an Internal Stage.
  • STEP-5: Execute a PUT Command to Stage the Records in CSV Files.

Lyftrondata is a platform that can help you assemble a captive data lake, data warehouse, or migrate from your old database to modern cloud-based data. It is considered as world's best data-driven platform for data driven enterprises.

Snowflake enables the cloud database to automatically generate code changes to test and produce the environment to run the codes. DevOps helps the developers with the systematic merging of code alterations for the automation of running the codes for building and testing.

Lyftrondata automates the ETL/ELT data pipeline by extracting and loading the database within minutes for running into Snowflake for real-time analysis. It can be accessible for all Business Intelligence or Analytics tools. Lyftrondata offers reliable high-performance transformation using Snowflake. For more information tap 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 & Diana
Founders at LightsOnData
Speaker | Javed Syed
CEO of Lyftrondata

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