Analytics

Seven Ways to Gain Data Clarity in An Uncertain Climate

It’s been a rollercoaster ride for everyone over the last few years, with particular pressure on Chief Financial Officers (CFOs) to support CEOs steering their organizations through things none of us expected to experience in our lifetime. Unfortunately, with the financial markets going into turmoil over the last few months and consumers of all shapes and sizes starting to cut back on spending, the uncertainty isn’t going to stop anytime soon.

Computer Vision 101: What It Is and Why It Matters

10 years ago, it would be ridiculous for people to believe that someday they would be able to use their faces to unlock their phones. That’s because it had been extremely difficult to create cartoon characters without profound drawing skills – but now we can easily turn photos into cartoon characters. Struggling with parallel parking? No worries, because self-parking systems are becoming standard equipment in vehicles.

Leveraging Data Analytics in the Fight Against Prescription Opioid Abuse

Every day in the US thousands of legitimate prescriptions for the opioid class of pharmaceuticals are written to mitigate acute pain during post-operation recovery, chronic back and neck pain, and a host of other cases where patients experience moderate-to-severe discomfort.

The Snowflake Telecom Data Cloud

As Snowflake rolls out its new Telecom Data Cloud, “Data Cloud Now” host Ryan Green sits down with Phil Kippen, Global Head of Industry, Telecom, at Snowflake, to discuss what it all means for telecom service providers. During the interview, Kippen notes that the arrival of 5G creates new market opportunities but also new operational complexities for telecom service providers as they take on the task of rolling out new services and managing new infrastructure. He explains that the rollout of the Telecom Data Cloud will help companies achieve operational efficiencies by providing a single, unified platform across all domains and business functions within the telecom service provider environment and across all clouds. In addition, Snowflake will help service providers create a new marketplace that will enhance their ability to find new ways to monetize their data and applications and will help them work with partners across the telecom ecosystem to develop new opportunities for collaboration and data sharing.

Implementing and Using UDFs in Cloudera SQL Stream Builder

Cloudera’s SQL Stream Builder (SSB) is a versatile platform for data analytics using SQL. As apart of Cloudera Streaming Analytics it enables users to easily write, run, and manage real-time SQL queries on streams with a smooth user experience, while it attempts to expose the full power of Apache Flink. SQL has been around for a long time, and it is a very well understood language for querying data.

Snowflake's Phil Kippen Weighs In on Launch of the Telecom Data Cloud

Today Snowflake is officially launching the Telecom Data Cloud. Snowflake’s newest Data Cloud helps telecommunications service providers break down data silos within the business and across the ecosystem, allowing organizations to easily and securely access data in near real time, enrich it with machine learning models, and then share and analyze it to drive better decision-making.

Reverse ETL - A Must-Have for Modern Businesses?

Extract, Transform, Load (ETL), and Extract, Load, Transform (ELT) pipelines are standard data management techniques among data engineers. Indeed, organizations have long been using these processes to create effective data models. However, there has recently been a remarkable rise in the use of Software-as-a-Service (SaaS) based customer relationship management (CRM) apps, such as Salesforce, Zendesk, Hubspot, Zoho, etc., to store and analyze customer data.

The Evolution from DevOps to DataOps

By Jason Bloomberg, President, Intellyx Part 2 of the Demystifying Data Observability Series for Unravel Data In part one of this series, fellow Intellyx analyst Jason English explained the differences between DevOps and DataOps, drilling down into the importance of DataOps observability. The question he left open for this article: how did we get here? How did DevOps evolve to what it is today, and what parallels or differences can we find in the growth of DataOps?