Analytics

Organizations may struggle with getting the full value of their data without knowing it. Their data science team uses data warehouses to power business intelligence solutions to create reports, dashboards, and other data visualizations. However, the time it takes for this information to reach teams makes it difficult to use for daily decision-making. Operationalizing data warehouses sends these insights directly into daily operations systems, thereby allowing for immediate access.

My name is Shanmukha Kota and I am a recent graduate from University at Buffalo. I interned with Cloudera last summer and joined Cloudera as a software engineer a couple of weeks ago and this is my first experience with CDP and CDP Operational Database. For a new hire college graduate in the industry with only academic experience with HBase, I can only say it is very simple and easy to set up and work with CDP Operational Database.

Real-time analytics can keep you up-to-date on what’s happening right now, such as how many people are currently reading your new blog post and whether someone just liked your latest Facebook status. For most use cases, real-time is a nice-to-have feature that won’t provide any crucial insights. However, sometimes real-time is a must. Let’s say that you run a big ad agency.

Most companies offering any kind of service or product answer this question from consumers or potential clients all the time: "How much does it cost?" Or, the much harder question: "How much will it cost if I choose these services with these extras for my particular company/house/yard/situation, etc.?" The tough part is that pricing services or software usually involves too many variables.

Chronicle, Google Cloud’s security analytics platform, is built on Google’s infrastructure to help security teams run security operations at unprecedented speed and scale. Today, we’re excited to announce that we’re bringing more industry-leading Google technology to security teams by integrating Chronicle with Looker and BigQuery.

Working with raw or unprocessed data often leads to poor decision-making. This explains why data scientists, engineers, and other analytic professionals spend over 80% of their time finding, cleaning, and organizing data. Accordingly, the ETL process - the foundation of all data pipelines - devotes an entire section to T, transformations: the act of cleaning, molding, and reshaping data into a valuable format.

The ETL (extract, transform, load) process depends on quickly, efficiently, and securely transferring information between sources and targets. However, there are multiple options for data transfer protocols, including FTP and its close relative SFTP. So what’s the difference between FTP and SFTP, and how can you decide which one to use for your enterprise data? We have all the answers below.