Why you should use Fivetran history mode for historical analysis over alternative solutions.

It's important to understand the uses and abuses of streaming infrastructure. Apache Kafka is a message broker that has rapidly grown in popularity in the last few years. Message brokers have been around for a long time; they're a type of datastore specialized for "buffering" messages between producer and consumer systems. Kafka has become popular because it's open-source and capable of scaling to very large numbers of messages.

With automated data integration, CaliberMind uncovers data insights for customers. As a Customer Data Platform (CDP), CaliberMind delivers data-driven insights to its customers. To do so, it must connect to its customers’ data sources, extract, process and transform the data, run it through specially designed analytic models, and, finally, present data back to the customer as insights. CaliberMind uses Fivetran to offload the task of ingesting data from its customers’ applications.

With Fivetran and Databricks, Slice reallocates the efforts of three data engineers to mission-critical projects and adds a data science team.

A data pipeline is a series of actions that combine data from multiple sources for analysis or visualization. In today’s business landscape, making smarter decisions faster is a critical competitive advantage. Companies desire their employees to make data-driven decisions, but harnessing timely insights from your company’s data can seem like a headache-inducing challenge.

Thinking of building out an ETL process or refining your current one? Read more to learn about how ETL tools give you time to focus on building data models. ETL stands for extract-transform-load, and is commonly used when referring to the process of data integration. Extract refers to pulling data from a particular data source. Transforms are used to make that data into a processable format. Load is the final step to drop the data into the designated target.

Want to look at how data has changed over time? Simply enable history mode, a Fivetran feature that data analysts can turn on for specific tables to analyze historical data. The feature achieves Type 2 Slowly Changing Dimensions (Type 2 SCD), meaning a new timestamped row is added for every change made to a column. We launched history mode for Salesforce in May and have been delighted with the response.

Bucketing, also known as binning, is useful to find groupings in continuous data (particularly numbers and time stamps). While it’s often used to generate histograms, bucketing can also be used to group rows by business-defined rules. I’ll walk through the simple bucketing various data types as well as custom buckets.

Given the competitive value of analytics and rapid adoption rates across industries, you can’t afford a subpar analytics program. In the late 90s, Oakland Athletics general manager Billy Beane used data to discover undervalued talent and assemble a perennial playoff-caliber team, and he did so on a shoestring budget compared to Major League Baseball’s heavy hitters. Beane’s pioneering use of data analytics became the subject of the bestselling book Moneyball.

Many analytics programs struggle to assimilate data from numerous and unpredictable sources, but automated ELT offers a solution. Why do so many businesses struggle to establish successful analytics programs? A lack of data is not the problem. Data volumes — from hundreds of cloud applications to millions of IoT endpoints — are exploding across organizations and industries.