This article gives you an overview of Cloudera’s Operational Database (OpDB) performance optimization techniques. Cloudera’s Operational Database can support high-speed transactions of up to 185K/second per table and a high of 440K/second per table. On average, the recorded transaction speed is about 100K-300K/second per node. This article provides you an overview of how you can optimize your OpDB deployment in either Cloudera Data Platform (CDP) Public Cloud or Data Center.

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.

Powered by Fivetran (PBF) is a new offering for modern data insights platforms that provide analytics-as-a-service companies. These firms build data products on top of disparate solutions such as Tableau, Snowflake and Redshift, and offer insights to decision-makers in diverse verticals, from finance and marketing to energy and transportation.

As organizations look to get smarter and more agile in how they gain value and insight from their data, they are now able to take advantage of a fundamental shift in architecture. In the last decade, as an industry, we have gone from monolithic machines with direct-attached storage to VMs to cloud. The main attraction of cloud is due to its separation of compute and storage – a major architectural shift in the infrastructure layer that changes the way data can be stored and processed.

In the lifecycle of a data warehouse in production, there are a variety of tasks that need to be executed on a recurring basis. To name a few concrete examples, scheduled tasks can be related to data ingestion (inserting data from a stream into a transactional table every 10 minutes), query performance (refreshing a materialized view used for BI reporting every hour), or warehouse maintenance (executing replication from one cluster to another on a daily basis).

Today, we’re announcing Data QnA, a natural language interface for analytics on BigQuery data, now in private alpha. Data QnA helps enable your business users to get answers to their analytical queries through natural language questions, without burdening business intelligence (BI) teams. This means that a business user like a sales manager can simply ask a question on their company’s dataset, and get results back that same way.

The value of YouTube has grown significantly for companies looking to bolster their brands with video content. The YouTube API is report-based, and its prebuilt reports fall into one of two categories: channel reporting and content owner reporting. Channel reports refer to the videos on a specific YouTube channel, while content owner reports contain data on all the channels owned by a particular individual.

Our 1.2.0.0 release of Cloudera Streaming Analytics Powered by Apache Flink brings a wide range of new functionality, including support for lineage and metadata tracking via Apache Atlas, support for connecting to Apache Kudu and the first iteration of the much-awaited FlinkSQL API. Flink’s SQL interface democratizes stream processing, as it caters to a much larger community than the currently widely used Java and Scala APIs focusing on the Data Engineering crowd.

Copyright quickly migrates its on-prem databases into Snowflake cloud using Fivetran. By implementing the modern data stack, and decommissioning numerous legacy platforms, Copyright estimates a savings of $40,000 a year and finds it much easier to manage the workload.

In recent years, there’s been an increasing demand for data scientists left and right, across industries and across departments. In the same vein, companies are getting more and more data than they know what to do with. In fact, according to IBM, 90% of the data in the world today has been created in the last two years alone. To put this influx to good use, organizations are turning to data scientists.