Systems | Development | Analytics | API | Testing

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.

Modern digital users have the patience level of a typical 5-year-old. Apps that incorporate software testing tools have one core objective to meet: not to make the first encounter with a glitchy UI and unusable functionalities. Software quality in software engineering is a general expectation business stakeholders have. Unfortunately, testing is also “sacrifice” for time to market, postponing the release date and rejecting builds last minute in the development process.

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.

Kong Summit has come and gone! Whether you joined us in San Francisco or missed this year’s big shindig, here are some of the highlights from the event and a round-up of all the Kong news dropped over the past two days. This post will continue to be updated with more details and links to videos from sessions as they’re available.