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.
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.
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.
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.
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.
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).
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.
