In this article, I’ll walk you through the process of building a machine learning model using BigQuery ML. As a bonus, we’ll have the chance to use BigQuery’s support for spatial functions. We’ll use the New York City taxicab dataset, with the goal of predicting taxi fare, given both pick-up and drop-off locations for each ride — imagine that we are designing a trip planner.
July was a huge month for merger and acquisition (M&A) activity in the BI space. Thoughtspot raised $145 million, Salesforce acquired Datorama, Workday acquired Stories.bi and Tableau acquired Empirical Systems.
We’re delighted that Yellowfin has been awarded a place on the public sector’s G-Cloud 10 (G10) framework, which enables us to supply British public sector organisations with the Yellowfin platform more easily.
In this month’s installment of What’s Happening in BigQuery, we’re sharing new features intended to make your life easier: some make BigQuery more performant and more cost effective, while others, like BigQuery ML, enable groundbreaking analysis tools in a cloud data warehouse that’s a first of its kind. First off, we just finished Next ‘18, our annual event focused on all things cloud.
Redwood City, CA - August 16, 2018 - Talend(NASDAQ: TLND), a global leader in cloud integration solutions, unveiled a new bulk data uploader for Microsoft Azure SQL Data Warehouse, which helps empower thousands of users across an organization with a fast, flexible and secure cloud data warehouse.
Our financial year came to a close at the end June and Yellowfin had another solid year of growth. There was consistent growth across all regions – in Europe, Japan, APAC and the US.
Continuous integration, delivery and deployment, known as CI/CD, has become such a critical piece in every successful software project that we cannot deny the benefits it can bring to your project. At the same time, containers are everywhere right now and are very popular among developers. In practice, CI/CD delivery allows users to gain confidence in the applications they are building by continuously test and validate them.
There is a common misconception about data-informed decision making. It goes that once we implement the right tools, and figure out how to analyze the data correctly, the data will automatically turn into insights and will translate to better business decisions. It sounds great in theory.
When I speak to people who are thinking about implementing BI, they are often overwhelmed by all the things they could measure. Many start by wanting to measure everything, which doesn’t necessarily help them. That’s because there’s an inherent cost in measuring things – everything you report and track creates an ongoing burden that your organization has to maintain. That’s why it’s important to be selective about what you measure from the get-go.
In our last blog, we talked about developing data processing jobs using Apache Beam. This time we are going to talk about one of the most demanded things in modern Big Data world nowadays – processing of Streaming data. The principal difference between Batch and Streaming is the type of input data source. When your data set is limited (even if it’s huge in terms of size) and it is not being updated along the time of processing, then you would likely use a batching pipeline.
