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.
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.
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.
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.
F5 Networks modernized its data stack, boosted time to insight, and placed actionable data in the hands of the right decision-makers. F5 Networks is a Seattle-based application services and application delivery networking company. Because its revenue depends on speed and accuracy, the company is always looking for ways to improve business insights and support data-driven decision-making.
Understand the impact of data transfer and egress costs across Microsoft Azure, Amazon Web Services and Google Cloud Platform. One of the questions most frequently asked by cloud-savvy, price-aware customers goes something like this: OK, so we like that your tool makes it easy to integrate our cloud database and storage in our centralized data warehouse, but I know our budget will be scrutinized for total cost of ownership (TCO), including our data egress costs.
Fivetran co-founder and CEO George Fraser shares the importance of the modern data stack and five developments he’s eagerly following. When Thomas Edison switched on his first working light bulb, even he could not have predicted that this new technology would eventually revolutionize every aspect of modern life.
When record-breaking demand exploded with COVID-related purchases, e-Commerce company Drizly thrived thanks to its modern data stack. Drizy is the world’s largest online alcohol marketplace. From the company’s website and app, users can order their favorite beers, wines and liquors from local retailers, and have them delivered in less than an hour.
Understand the impact of data transfer and egress costs across Azure, Amazon Web Services, and Google Cloud platform in data integration One of the most frequent questions asked by cloud-savvy, price-aware customers is something like: Ok, so we like that your tool makes it easy to integrate our cloud database and storage in our centralized data warehouse, but I know our budget will be scrutinized for Total Cost of Ownership (TCO), including our data egress costs.
