At Google Cloud, we’re invested in building data analytics products with a customer-first mindset. Our engineering team is thrilled to share recent feature enhancements and product updates that we’ve made to help you get even more value out of BigQuery, Google Cloud’s enterprise data warehouse.
We live in a Fourth Industrial Revolution, where data is the lifeblood of business. Those of us who harness the power of artificial intelligence, machine learning and augmented analytics to uncover insights from data are the ones who will be able to find better ways of driving efficiency, productivity and superior business outcomes.
The data age has been marked by numerous “hype cycles.” First, we heard how Big Data, Data Science, Machine Learning (ML) and Advanced Analytics would have the honor to be the technologies that would cure cancer, end world hunger and solve the world’s biggest challenges. Then came third-generation Artificial Intelligence (AI), Blockchain and soon Quantum Computing, with each one seeking that honor.
Through automated data pipelines, analysts can access the data they need and engineering teams can complete higher-value projects.
Looking for a Segment and Fivetran comparison? This article covers some fundamental differences between a specialized data integration tool and a customer data platform.
I used the K6 load testing framework to benchmark the Compute Engine f1-micro and Caddy web server hosting this site. With CloudFlare caching turned off, the server was able to serve an onslaught 800 virtual users continuously reloading the page (while maintaining a median request duration of <400ms), but started dropping requests when increasing the load further. This is fine.
In traditional software development projects with clear departments and defined roles, people tend to see their responsibilities as limited to the boundaries of their position. Siloed teams are not able to see the systemic view of the whole application delivery since: Siloed departments lose the vast opportunity to help and collaborate with each other and prevent problems and bugs from happening in the first place.
They say that procrastination is the thief of time. In the world of software development, there are some additional “time thieves” that prevent our teams from developing new features or slow them down as they attempt to fix issues. As software engineers or R&D managers, we take it for granted that our teams spend a lot of their time waiting for compiling, testing, and deploying.
Data Discovery and Exploration (DDE) was recently released in tech preview in Cloudera Data Platform in public cloud. In this blog we will go through the process of indexing data from S3 into Solr in DDE with the help of NiFi in Data Flow. The scenario is the same as it was in the previous blog but the ingest pipeline differs. Spark as the ingest pipeline tool for Search (i.e.
