Systems | Development | Analytics | API | Testing

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.

As you may know, event logs are a common feature in operating systems and other software that keep track of system and application errors. When you have API traffic to follow or front-end actions you want to watch, using Moesif’s Live Event Log is a simple way to filter and find the data you need.

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.

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.

Logging can be a life-saver when it comes to discovering bugs or faults in your Go (Golang) code. The three most popular ways to log errors in Golang are: This article will walk you through how to log errors using each method, when and why you’d want to use each, along with examples.

The Python KeyError is an exception that occurs when an attempt is made to access an item in a dictionary that does not exist. The key used to access the item is not found in the dictionary, which leads to the KeyError.

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.

Financial services innovation continues to progress at a breakneck pace. For example, fintech developers can programmatically spin up accounts, move money, and issue and manage cards with Increase or embed financial services into their marketplace with Stripe – capabilities that were unimaginable just a few years ago.

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use. According to Gartner, by 2023 65% of the world’s population will have their personal data covered under modern privacy regulations.