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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.

Here’s what you need to know about how to use your customer data platform (CDP) as your data warehouse: Whether you’re a mom-and-pop store or an ecommerce giant, understanding the customer journey is crucial to your organization’s success. When you collect data across a wide range of customer touchpoints, you can use this wealth of information for many different use cases: performing audience segmentation, improving your marketing campaigns, boosting customer engagement, and more.

Every organization wants to identify the right sales leads at the right time to optimize conversions. Lead scoring is a popular method for ranking prospects through an assessment of perceived value and sales-readiness. Scores are used to determine the order in which high-value leads are contacted, thus ensuring the best use of a salesperson’s time. Of course, lead scoring is only as good as the information supplied.

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.

Databases are important for every organization. Learn more about what databases are and the different types available.

Tell us if this sounds familiar. You’ve found an awesome data set that you think will allow you to train a machine learning (ML) model that will accomplish the project goals; the only problem is the data is too big to fit in the compute environment that you’re using. In the day and age of “big data,” most might think this issue is trivial, but like anything in the world of data science things are hardly ever as straightforward as they seem.

It has been months ago when Toloka and ClearML met together to create this joint project. Our goal was to showcase to other ML practitioners how to first gather data and then version and manage data before it is fed to an ML model. We believe that following those best practices will help others build better and more robust AI solutions. If you are curious, have a look at the project we have created together.

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.

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.