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.
Migrating a data warehouse from a legacy environment requires a massive upfront investment in resources and time. There is a lot to consider before and during migration. You may need to replan your data model, use a separate platform for tasks scheduling, and handle changes in the application’s database driver. Therefore, organizations must take a strategic approach to streamline the process. This article presents a step-by-step approach for migrating a data warehouse to the cloud.
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.
Implementing a modern, cloud-based analytics stack doesn’t have to be hard — you can do it in three steps, actually. Implementing a modern data stack (MDS) — data integration tool, cloud data warehouse and business intelligence platform — is the best way to establish a successful analytics program as data sources and data volumes multiply.
Learn the how and what of analytics and data integration. This is the first in a two-part abridged version of The Essential Guide to Data Integration. Read Part 2 here, and get the full book for free here! You can also watch the webinar. What is data analytics How do you integrate data? Should you build or buy a data analytics solution? What are some business and technical considerations for choosing a data analytics tool, and how can you get started? Let’s start with the first two questions.
Without an overarching company data culture, even the best technology tools won’t get you where you want to go, say the co-founders of Data Culture. Data isn’t just a tech solution. For Gabi Steele and Leah Weiss, founders of the consultancy Data Culture, it’s also a “people” solution. Even within companies that enthusiastically embrace a cloud-based modern data stack, a substantial gap often exists between the business and data sides of the organization.
Welcome Technologies builds more robust data pipelines with Fivetran to propel its work on improving the lives of immigrants through a data-first approach. Key Takeaway With its data-first approach, Welcome Tech is developing machine learning and security models to better serve the immigrant community. After building and maintaining a Postgres connector, Welcome Tech brings on Fivetran to scale its data architecture.
We’re honored to win this prestigious award, and we’re doubling down on the Lakehouse architecture with Databricks SQL analytics plans. Fivetran is the proud recipient of the Databricks ISV Partners Innovation Award as announced at this week’s Data & AI Summit Europe event. The award recognizes how Fivetran has collaborated with Databricks to empower data professionals to accelerate time to insights with Delta Lake and the Lakehouse architecture for the modern data stack.
