Analytics

This summer marks five years since we announced our first fully managed connector on Confluent Cloud in 2019, the Amazon S3 Sink Connector. Since then, our connector offerings have not only expanded significantly but also enabled teams to send hundreds of petabytes of data throughput. Today, we support over 80 pre-built, fully managed connectors, custom connectors, and secure private networking.

Digitalization, as we’re talking about in this article, refers to the strategic use of digital technologies and analytics to shift customer interactions from physical locations to digital platforms like apps and online services.

While Salesforce is a much celebrated CRM tool in the industry, it’s no perfect tool just like any others. It offers limited flexibility for on-the-fly data manipulation compared to Excel. Salesforce can be complex to set up and use effectively, particularly for users without technical expertise. It often requires training or support from Salesforce administrators to fully utilize its capabilities.

Your organization is likely sitting atop vast quantities of diverse data. However, all that data must undergo certain processes before you can fully leverage it. Data discovery is among those processes. Let’s learn more about it.

Workflows determine how organizations conduct processes. These workflows can initially be very straightforward—just a simple series of steps that must be completed sequentially to achieve a particular outcome. However, the steps can become more complicated as business processes and requirements become more extensive. Your organization needs to keep these workflows running smoothly even as they become more complicated.

Smooth and effective data migration helps organizations move data across systems efficiently to maintain their competitive advantage. Still, Gartner reports that only 17% of initiatives involving data migration are completed within their budgets or set timelines. Understanding these data migration challenges is the first step toward overcoming them. In this blog, we’ll explore data migration and its different types, challenges, and strategies for dealing with them.

Organizations today are accumulating data more than ever. Traditional data management approaches, such as centralized data warehouses and siloed data marts, are struggling to keep pace with the ever-increasing volume, velocity, and variety of information. The complexity of modern data environments is outpacing the capabilities of these legacy systems and demands a more agile and distributed solution.

Ramkumar Nottath, the Senior Solutions Architect at AWS, beautifully puts it. And that’s where low-code or no-code ETL platforms can help—to make the data consumable and democratize it. In this blog, I explain low-code vs. no-code from my experience.