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ETL

Are These the 6 Best Reverse ETL Vendors for 2021?

The amount of big data that enterprises churn out is simply staggering. All this information is worthless unless organizations unlock its true value for analytics. This is where ETL proves useful. Traditional ETL (extract, transform, and load) remains the most popular method for moving data from point A to point Z. It takes disparate data sets from multiple sources, transforming that data to the correct format and loading it into a final destination like a data warehouse.

ETL vs ELT: 11 Critical differences

ETL and ELT refer to two patterns of data storage architecture within your data pipelines. The letters in both acronyms stand for: So both ETL (extract, transform, load) and ELT (extract, load, transform) processes help you collect data, transform it into a usable form and save it to permanent storage, where it can be accessed by data scientists and analysts to extract insights from the data. What is the difference?

Use Cases for Reverse ETL

According to Gartner, leading organizations in every industry are wielding data and analytics as competitive weapons. Companies that leverage data as a competitive differentiator will stand the best chance of acting faster on opportunities and responding to threats in a competitive marketplace. The problem is that most companies aren’t aware of the value of their data. As a result, they aren’t leveraging the full potential of their data to make informed decisions.

What Scenario Should You Use CDC for?

Sometime in 2019, Netflix cracked a conundrum that stumped them for years. The company had so much data about its content and subscribers, it had to sync multiple heterogeneous data stores like MySQL and Elasticsearch continuously, which brought seriously stressful challenges like dual writes and distributed transactions. So Netflix created its own CDC tool that processes captured log events in sequence and takes dumps for specific tables and primary keys of tables. Problem sorted. Case closed.

Reverse ETL to NetSuite

Reverse ETL is a data integration technology that offers a wonderful way to enable solutions for making various stored data more actionable and usable. This process is especially helpful for enterprise business operation tools that help teams execute processes and meet goals more effectively. The idea is to use clean and accurate data to enhance various SaaS platforms and business management tools to enhance processes.

Change Data Capture: CDC for E-Commerce

Change data capture is one of the fundamental underpinnings of modern data management. Without knowing when their enterprise data has changed or refreshed with new information, businesses wouldn’t be able to access up-to-the-minute insights that help them stay competitive in a constantly shifting landscape. In change data capture (CDC), users are promptly notified (either in real-time or near real-time) when changes have been made to a source table or source database.

How to Operationalize your Data Warehouse with Reverse ETL

Organizations are losing out on data-driven decision-making opportunities when data stays in the data warehouse. While business intelligence solutions can surface insights from these data sets, it often reaches team members too late to be used for daily business operations. Reverse ETL empowers organizations to increase the value of their data warehouses through operationalization. Learn how this can transform the way companies use data and insights.

The Importance of CDC for ETL

The growth of corporate data and the need for more corporate applications and systems are not trends that will soon slow down. Data has become an essential component of commercial success and a measure of the value of a company. Investing in platforms, processes, and people that can effectively protect, transform, and leverage data is the hallmark of a modern data-driven enterprise.

Transforming Customer Data for Salesforce

CRM (customer relationship management) software is the lifeblood of any modern B2C company. By monitoring and storing all of your interactions with prospects and customers—from their first visit to your website to their most recent purchase—CRM software makes it dramatically easier to segment your customer base, identify hidden trends in the data, make smarter predictions, and forecasts, and much more.

Building an ETL Pipeline in Python

Thanks to its user-friendliness and popularity in the field of data science, Python is one of the best programming languages for ETL. Still, coding an ETL pipeline from scratch isn’t for the faint of heart — you’ll need to handle concerns such as database connections, parallelism, job scheduling, and logging yourself. The good news is that Python makes it easier to deal with these issues by offering dozens of ETL tools and packages.