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As your data volumes grow, your operations slow down. Data ingestion - extraction of all underlying datasets, transformation, and loading in a storage destination (such as a PostgreSQL or MySQL database) - becomes sluggish, impacting processes down the line. Affecting your data analytics and time to insights. Change Data Capture (CDC) makes data available faster, more efficiently, and without sacrificing data accuracy. In this blog we are going to overview the 7 best change data capture tools of 2022.

Keboola and ThoughtSpot partnered up to offer click-and-launch insights machines. With the original integration, you can already cut the time-to-insight. Keboola helps you get clean data and ThoughtSpot helps you turn it into insights. What’s new? The new solution builds out-of-the-box and ready-to-use data pipelines (Keboola Templates) and live self-serve analytic dashboards (ThoughtSpot SpotApps) from the ground up. You just need to click-and-launch your analytic use case.

Principal Component Analysis (PCA) is one of the most commonly used unsupervised machine learning algorithms across a variety of applications: exploratory data analysis, dimensionality reduction, information compression, data de-noising, and plenty more. In this blog, we will go step-by-step and cover: Before we delve into its inner workings, let’s first get a better understanding of PCA. Imagine we have a 2-dimensional dataset.

The Extract, Transform, and Load process (ETL for short) is a set of procedures in the data pipeline. It collects raw data from its sources (extracts), cleans and aggregates data (transforms) and saves the data to a database or data warehouse (loads), where it is ready to be analyzed. A well-engineered ETL process provides true business value and benefits such as: Novel business insights. The entire ETL process brings structure to your company’s information.

Star schemas and snowflake schemas are the two predominant types of data warehouse schemas. A data warehouse schema refers to the shape your data takes - how you structure your tables and their mutual relationships within a database or data warehouse. Since the primary purpose of a data warehouse (and other Online Analytical Processing (OLAP) databases) is to provide a centralized view of all the enterprise data for analytics, data warehouse schemas help us achieve superior analytic results.

Don’t sacrifice scalability for savings - have it both ways When left unchecked, the cumulative costs of your company data can ramp up fast. From training CPU-intensive machine learning algorithms that aren’t used in production to supporting enormous databases storing every minute event “just in case”. Letting your data operating costs run without checks and balances can quickly cause costs to bloat beyond your allocated budgets.

Whether you are trying to establish a new niche, carve out a bigger share of a mature and competitive market, or increase the value of your existing products or services, data can be used to gain a competitive advantage and increase your revenue. In this article, we will establish what data monetization is, how it translates to business use cases, showcase how it impacts business performance, and offer guidance on how to start monetizing data today.

Since the beginning, Keboola has been designed to be a world-class data platform as a service. Based on the results of this season’s G2 statistics, we have succeeded yet again. The purpose of the Keboola platform is to make our customers’ data processing simple, reliable, and transparent throughout the company. We love our customers and value the feedback they've given our teams over the years.

The modern enterprise taps into over 400 different data sources to extract the insights that sharpen its competitive edge. The complexity, though, does not stop at the origin, where data is generated. To get valuable insights from raw data enterprises must extract data from its source, transform the data (clean and aggregate it), and finally load the data into a data warehouse or BI tool, where it is served to data scientists for analysis.

We previously wrote how the data mesh architecture rose as an answer to the problems of the monolithic centralized data model. To recap, in the centralized data models, ETL or ELT data pipelines collect data from various enterprise data sources and ingest it into a single central data lake or data warehouse. Data consumers and business intelligence tools access the data from the central storage to drive insights and inform decision-making.