A data extraction tool can help you speed up one of the most error-prone engineering processes: collecting raw data from different sources. In this article, we are going to analyze the following 6 market leaders in data extraction: Before we dive in, let’s look at all the problems you can avoid by implementing a data extraction tool.

The data transformation process reshapes data from a raw mess to a business goldmine by: Using a data transformation tool streamlines the entire process and saves you time and energy on these valuable but tedious tasks. In this article, we’ll explore the 7 best tools on the market for data transformations. Each tool will be evaluated with Pros, Cons, and a clear decision for who this tool is best for. Are you in a hurry?

Snowflake is a cloud-based software-as-a-service (SaaS) data warehouse. It offers separate and on-the-fly scalable services for data storage, compute, and analytics.

When you look closer at the Modern Data Stack (MDS) you need to brace yourself. The number of tools companies use for their databases, user administration, data extraction, data integration, security, machine learning, and a myriad of other use cases has grown astronomically. Matt Turck, VC at FirstMark, composes a yearly infographic of the hot tools in the datascape: And this is just a shortlist of both the most popular and fastest-growing tools.

Gaining insights from your data can be time-consuming. Or as simple as a few clicks. Depending on if you want to do everything by yourself, or if you let us help. For example, next time one of your stakeholders asks: “Can you deliver the dashboards by the end of next week?” You can say yes with confidence, and in this blog we are going to show you why and how it is done.

Oh boy, it’s been more than a year again since my last HKBFUKC article (yep, that’s a new standard abbreviation). This is the fourth article in the series. You can always check out the first, second and third on our blog. Again, loads of stuff has happened since the last time. I made the top 16 at the 4 Seasons MTG Legacy tournament in Bologna, I visited Lego House in Billund and I got married!

Planetly uses technology to simplify carbon management for companies at scale. Their data-driven software solution helps companies reach net-zero emission targets in four steps: The entire carbon management life cycle is powered and fueled by data. We talked to Cari Davidson, VP of Engineering and Patricia Montag, the Engineering Lead Analytics, to better understand what role Keboola (and data as a whole) play in the company’s operations and what that means for the engineering team.

Over the years, Keboola and Snowflake have seen their own share of successes and incredible achievements. Now, we can proudly announce that Keboola has joined the Powered by Snowflake program. With both companies founded around the same time, Keboola and Snowflake have been working hand in hand for some time now.

The data landscape is exploding with tools. As data professionals we have at our fingertips specialized tools for anything: from specialized databases (graph, geo, you name it) to tools for SQL-driven transformations (looking at you, dbt). Yet, a lot of data work is about provisioning, selecting, administering, and just maintaining those tools. Which is just a pain. As Pavel Dolezal, CEO and co-founder of Keboola said: The answer is in how the Modern Data Architecture is built.

Data integration is the data engineering process of combining data across all the different sources in a company (CRM, SaaS apps like Salesforce, APIs, …) into a single unified view. The data integration process includes data extraction, data cleansing, data ingestion, data validation, modeling, and exposing ready-to-be-consumed data sets to other users and applications for business intelligence or data-driven activities.

The data pipeline is at the heart of your company’s operations. It allows you to take control of your raw data and use it to generate revenue-driving insights. However, managing all the different types of data pipeline operations (data extractions, transformations, loading into databases, orchestration, monitoring, and more) can be a little daunting. Here, we present the 7 best data pipeline tools of 2022, with pros, cons, and who they are most suitable for. 1. Keboola 2. Stitch 3. Segment 4.

Is repetitive and menial work impeding your data scientists, analysts, and engineers from delivering their best work? Consider automating your data analytics to free their hands from routine tasks so they can dedicate their time to doing more meaningful, creative work that requires human attention. In this blog we are going to talk about: Now let’s dive in.

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.

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.