Win More Deals with Redshift, Salesforce & - Webinar

Instead of the normal ETL or ELT process, we’ll do the opposite, Reverse ETL. Using Reverse ETL allows you to pull customer data from your data warehouse, then push data back into your CRM to empower your sales team. Learn how to enhance your lead data and win more deals during this webinar.

E-Commerce Reverse ETL

If you own an e-commerce website, you need to remain competitive and increase your capabilities in the process. One of the latest and greatest solutions right now is to harness the power of reverse ETL. The abbreviation ETL stands for Extract, Transform, Load, and is one of the best ways to connect business apps and platforms. This ETL tool offers multiple opportunities to transfer your securely stored customer data from the modern data warehouse to multiple other apps and platforms.

Operationalize Your Data Warehouse With Reverse ETL

Data warehousing aggregates data from disparate sources so you can run real-time reports for greater business intelligence. However, a data warehouse does more than generate big data analytics. How about using it as a data source rather than just a destination? You can move data from your warehouse to other systems in your networks, such as Salesforce or Zendesk, and improve existing operations.

How To Use Change Data Capture with

Change data capture (CDC) is a crucial, but also tremendously underappreciated, feature that forms the backbone of modern ETL workloads. Without knowing which data has changed since you last accessed it, you’d be forced to extract all the data from a source table or database each time that you perform data integration—which would be a tremendously inefficient process.

Modern Data Stack using for the ELT is a company that provides an ELT (Extract, Load and Transform) data stack. They can do transformations using DBT, which stands for Database Transformation toolkit. Then they use again to push the data into systems like Salesforce. This system will allow you to have better control over your data and provide a cost-effective solution.

Is SSIS a Good ETL Tool?

ETL (Extract, Transfer and Load) is a well-known data integration process. There is an overwhelming number of tools that you can use (one of which is SSIS) and it can be difficult to choose between them. What exactly is SSIS, and how can it help your company perform ETL better than you ever have before? This article will explain the major features of SSIS, demonstrate the pros and cons of implementing it, and advise as to when you might be better off with a different ETL tool.

What Are The Best ETL Tools For Vertica?

Vertica claims to offer the "most advanced unified analytical warehouse" in the world, providing actionable data insights you can't find anywhere else. The truth is, like any data warehouse, Vertica is only as good as the data you put into it. Moving data to Vertica can be a headache for organizations without a data engineering team. Data might live in various locations — transactional databases, relational databases, customer relationship management (CRM) systems, you name it.

The Ultimate Guide to Redshift ETL: Best Practices, Advanced Tips, and Resources for Mastering Redshift ETL

Amazon Redshift makes it easier to uncover transformative insights from big data. Analytical queries that once took hours can now run in seconds. Redshift allows businesses to make data-driven decisions faster, which in turn unlocks greater growth and success. For a CTO, full-stack engineer, or systems architect, the question isn’t so much what is possible with Amazon Redshift, but how. How do you ensure optimal, consistent runtimes on analytical queries and reports?

Top 7 ETL Tools for 2022

Organizations of all sizes and industries now have access to ever-increasing amounts of data, far too vast for any human to comprehend. All this information is practically useless without a way to efficiently process and analyze it, revealing the valuable data-driven insights hidden within the noise. The ETL (extract, transform, load) process is the most popular method of collecting data from multiple sources and loading it into a centralized data warehouse.