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For data analysts, business intelligence professionals, and CTOs to optimize and scale business operations, they must first understand the business data that is available to them. One of the best platforms to turn complicated data points from multiple platforms into a singular, coherent data set is Microsoft Power BI. However, you must first prepare the data sets to eliminate fragmentations and create structural consistency. This article explores how to prepare data for Microsoft Power BI.

Hortonworks DataFlow (HDF) 3.5.2 was released at the end of 2020. The new releases will not continue under HDF as Cloudera brings the best and latest of Apache NiFi in the new Cloudera Flow Management (CFM) product. Getting the latest improvements and new features of NiFi is one of many reasons for you to move your legacy deployments of NiFi on this new platform. To that end, we released a few blog posts to help you migrate from HDF to CFM.

In my previous blog posts, I’ve talked about how you can aggregate data depending on the data type, as well as how you can re-express your data to get more value from it. For this post, let’s look at some of the different ways of measuring your data.

Ask any analyst how they spend the majority of their work day and they’ll tell you: Performing remedial tasks that provide no analytics value. 92% of data workers report that their time is being siphoned away performing operational tasks outside of their roles. Data teams waste an inordinate amount of time maintaining the delicate data-to-dashboards pipelines they’ve created, leaving only 50% of their time to actually analyze data.

Data paves the way for every strategic move made by banks and insurance companies. Whether looking to create a new service, complying with regulations, or overhauling and re-engineering legacy operations, a massive data project is always central to the effort. For financial services businesses, the pace at which they can reshape and repurpose data has become a key determinant of their ability to predict market trends and meet client expectations.

Last week in our BigQuery Reference Guide series, we spoke about the BigQuery resource hierarchy - specifically digging into project and dataset structures. This week, we’re going one level deeper and talking through some of the resources within datasets. In this post, we’ll talk through the different types of tables available inside of BigQuery, and how to leverage routines for data transformation.

When it comes to anomaly detection, one of the key challenges that many organizations face is that it can be difficult to know how to define what an anomaly is. How do you define and anticipate unusual network intrusions, manufacturing defects, or insurance fraud? If you have labeled data with known anomalies, then you can choose from a variety of supervised machine learning model types that are already supported in BigQuery ML.

9.6 is focused on Yellowfin features that enhance the way our customers build, design and embed stunning analytical content, which include data storytelling, augmented analytics, actionable dashboards — and provide a high ease-of-use experience. As always, you can read the full list of updates in our release notes page, and view our release highlights video below to see the new enhancements demonstrated.

During the product keynote at our recent QlikWorld online event, we unpacked the power of the analytics data pipeline to transform raw data into informed action. Imagine a data pipeline where information flows continuously into everyday processes, allowing your organization to seize every business moment, as it happens...