SaaS in 60 - New Qlik Application Automation Connectors

Recently we added some Data Warehouse connectors for Amazon Redshift, Google Big Query and Snowflake allowing your workflows to utilize data management operations such as inserts, deletions, updates, SQL queries and even API requests. We’ve also added a connector to work with our new automated machine learning environment AutoML as well as a number of remote application and event management connectors that work with Dbt, UI Path and Splunk.

API Management vs. API Gateways: What's the Difference?

Businesses of all sizes are increasingly relying on APIs to create competitive advantages, but many don't know the difference between API Management and API Gateways. This article will discuss what each solution does, how they can be used in conjunction with one another, and why it's essential to use both services when building an API strategy.

Security Reference Architecture Summary for Cloudera Data Platform

This blog will summarise the security architecture of a CDP Private Cloud Base cluster. The architecture reflects the four pillars of security engineering best practice, Perimeter, Data, Access and Visibility. The release of CDP Private Cloud Base has seen a number of significant enhancements to the security architecture including: Before diving into the technologies it is worth becoming familiar with the key security principle of a layered approach that facilitates defense in depth.

Expanding the Data Cloud with Apache Iceberg

The Snowflake Data Cloud is a powerful place to work with data because we have made it easy to do difficult things with data, such as breaking down data silos, safely sharing complex data sets, and querying massive amounts of data. As customers move to the Data Cloud, their needs and timelines vary—our goal is to meet every customer where they are on their Data Cloud journey.

Promo video - Demystifying DataOps - Riding the Change

A well planned data-driven strategy has become the need of the hour in today’s competitive world. In order to deliver the best, businesses need relevant, appropriate and sufficient data at the right time. But the characteristics of data are changing rapidly across volume, variety and variability, which puts enormous pressure on current data management approaches. Thus, dealing with real-time insights from increasing data in an automated manner has become the current focus of many experts. This has resulted in the rise of DataOps.

Best ETL Tools for Firebolt

Data integration is the core of digital transformation efforts. ETL (extract, transform, load) is what makes such integrations possible. It helps you move massive quantities of information efficiently between a wide variety of sources. Firebolt is a cloud data warehouse that helps companies manage huge volumes of data to support analytics for business insights. It offers high-performance storage and ease of scalability as a low-cost cloud-native solution.

Extracting and Visualizing Your Shopify Data (Part 1)

As technology continues to evolve and consume our daily lives, more consumers than ever before are turning to eCommerce options. Of course, one of the largest eCommerce platforms in the world is Shopify. While the Shopify platform has a lot to offer businesses, managing your Shopify store’s data alone can be quite challenging. The good news is that you don’t have to take on the complexities of data management alone.

Amazon Redshift: Comprehensive Guide

As the business world increasingly becomes dependent on technology, the way in which companies handle and store their data becomes even more important. Therefore, finding a safe and secure place to store company data is becoming a necessity in the digital age. One robust cloud data warehouse that has been helping many companies safely store their data is known as Amazon Redshift.

Growing AI Fast with ML-Ops: Breaking the barrier between research and production

AI models get smarter, more accurate, and therefore more useful over the course of their training on large datasets that have been painstakingly curated, often over a period of years. But in real-world applications, datasets start small. To design a new drug, for instance, researchers start by testing a compound and need to use the power of AI to predict the best possible permutation.