Announcing the Fivetran dbt Package for Asana
Improve your Asana task management for more efficient projects.
A Dose Of Data Science Demystification
Join two data engineers and analysts in pulling back the curtain on real customer engagements, showing how to select and implement advanced data science and analytic techniques. In this session we will discuss our implementation of two data science models at a large agricultural products manufacturer: a propensity-to-buy model and a recommendation engine. We will discuss how each of these models works and how they were implemented for our client.
Make Your Data Fabrics Work Better
To gain the full benefits of the DataOps strategy, your data lakes must change. The traditional concept of bringing all data to one place, whether on-premises or in the cloud, raises questions of timing, scale, organization and budget. The answer? Data fabric. It replaces traditional data lake organization concepts with a more flexible and economical architecture. In this session, we'll define what a data fabric is, show you how you can begin organizing around the concept, and discuss how to align it to your business objectives.
Introducing White Labeling: A Fully Branded Reporting Experience for Your Company and/or Clients
Provide fully-branded reports, performance alerts, log in access, and more to your team and/or clients.
Apache Hadoop YARN in CDP Data Center 7.1: What's new and how to upgrade
This blogpost will cover how customers can migrate clusters and workloads to the new Cloudera Data Platform – Data Center 7.1 (CDP DC 7.1 onwards) plus highlights of this new release. CDP DC 7.1 is the on-premises version of Cloudera Data Platform.
5 Challenges of Simplifying DevOps for Data Apps
The benefits of building a DevOps culture for software companies are clear. DevOps practices integrate once-siloed teams across the software development lifecycle, from Dev to QA to Ops, resulting in both faster innovation and improved product quality. As a result, most software development teams have deployed tools to enable DevOps practices across their workflow.
A Cloud Data Platform for Data Science
Data scientists require massive amounts of data to build and train machine learning models. In the age of AI, fast and accurate access to data has become an important competitive differentiator, yet data management is commonly recognized as the most time-consuming aspect of the process. This white paper will help you identify the data requirements driving today's data science and ML initiatives and explain how you can satisfy those requirements with a cloud data platform that supports industry-leading tools.
5 Strategies to Improve Secure Data Collaboration
Many organizations struggle to share data internally across departments and externally with partners, vendors, suppliers, and customers. They use manual methods such as emailing spreadsheets or executing batch processes that require extracting, copying, moving, and reloading data. These methods are notorious for their lack of stability and security, and most importantly, for the fact that by the time data is ready for consumption, it has often become stale.
Demand for Data Grows in Agriculture
Agriculture (Ag) is the oldest and largest industrial vertical in the world, and its importance continues to grow as it becomes more challenging for people to access healthy and fresh food. A recent Agriculture Analytics Market report, released by Markets and Markets, estimates that by 2023, the global agriculture analytics market size will grow from 585 million to 1.2 billion dollars as demands for real-time data analysis and improved operations increase.
GigaOm Data Pipeline Platform Comparison
With the proliferation of tools generating more available data to be collected, it’s becoming increasingly more and more important to automate your data pipeline to help you get to insights faster. GigaOm recently ran a report comparing a few automated data integration vendors, including Fivetran.