In a global economy, real-time data analysis is closely related to business success. Without data-driven insights, organizations find it challenging to remain competitive, improve company performance, and deliver strong user experiences, regardless of their industry. To match the pace of business, companies require transparent, data-driven relationships.

Medical devices have become increasingly complex as technology evolves, and the sheer number of these devices now being worn or implanted has grown exponentially over the past few years. There are currently over 500,000 different types of smart, connected medical devices in use that have the ability to collect, share, or store private patient data and protected health information (PHI)(1).

Coronavirus has impacted the travel industry, but as it adapts, there is one factor airlines have always worked hard to minimize: delayed flights. Arriving late or missing a connection can severely impact the customer experience, which is why airlines work hard to maintain high rates of on-time performance (OTP). To that end, pilots may have to use extra fuel to make up for a delayed departure or to reach a destination early, even if it means circling the airport before landing.

The next generation of 5G networks are unlocking a mind-bending array of new use cases. Blistering speed, super low latency, and access to more powerful mobile hardware bring VR, AR and ultra high-definition experiences into sharp focus for the near future. But there’s a bigger shift being driven by 5G, and it’s not actually about speed at all. It’s about re-thinking the modern telco business model.

Navistar is a leading global manufacturer of commercial trucks. With a fleet of 350,000 vehicles, unscheduled maintenance and vehicle breakdowns created ongoing disruption to their business. Navistar required a diagnostics platform that would help them predict when a vehicle needed maintenance to minimize downtime.

Strong investments in an organization’s data pipeline result in greater business outcomes. Few would dispute this claim, reflected in the massive growth in the big data and analytics market which continues to fuel many organizations’ ambition to become data-driven.

Apache Hive supports transactional tables which provide ACID guarantees. There has been a significant amount of work that has gone into hive to make these transactional tables highly performant. Apache Spark provides some capabilities to access hive external tables but it cannot access hive managed tables. To access hive managed tables from spark Hive Warehouse Connector needs to be used.

As a result of the COVID-19 pandemic, organizations around the world have had to transform overnight. Businesses that had been delaying digital transformation, or that hadn’t been thinking about it at all, have suddenly realized that moving their data analytics to the cloud is the key to coping with and surviving the COVID-19 disruption. The next phase is about rebounding and thriving in a post-COVID-19 world.

Over the last 25 years, I have an unparalleled front seat to the digital transformation that is now accelerating in the connected manufacturing and automotive industry. Not many people have had the opportunity to witness the transformation and be as active in this area as I have; I consider myself lucky.

Most of us know the story of “The Tortoise and the Hare.” It is one of Aesop’s classic fables in which a speedy, overconfident hare becomes complacent and realizes, all too late, that the tortoise, although outmatched, has managed to beat him in a race. It teaches us lessons about overconfidence and perseverance and has caused phrases like “slow and steady wins the race” to creep into our everyday language.

“You cannot be the same, think the same and act the same if you hope to be successful in a world that does not remain the same.” This sentence by John C. Maxwell is so relevant to rapidly changing cloud hosting technology. Businesses understand the added value and are looking at cloud technologies to handle both operational and analytical workloads.

The Cloudera Operational Database (COD) experience is a managed dbPaaS solution which abstracts the underlying cluster instance as a Database. It can auto-scale based on the workload utilization of the cluster and will be adding the ability to auto-tune (better performance within the existing infrastructure footprint) and auto-heal (resolve operational problems automatically) later this year.

A Shift Towards Industry 4.0 Is Improving Manufacturing Efficiency And Increasing Innovation In Part II of our series with Michael Ger, Managing Director of Manufacturing and Automotive at Cloudera, he looks in greater detail at how AI, big data, and machine learning are impacting connected living and the evolution of autonomous driving.

Cloudera’s Operational Database provides unparalleled scale and flexibility for applications, enabling enterprises to bring together and process data of all types and from more sources, while providing developers with the flexibility they need. In this blog, we’ll look into capabilities that make Operational Database the right choice for hyperscale.

Since the financial crisis of 2008, regulators have been consistently working to identify emerging risks that can potentially result in financial stability events. The growth in cloud adoption across the Financial Services Industry (FSI) and the associated increase in reliance on third-party infrastructure providers has gained the attention of regulators at global, regional, and national levels.

Approximately 300,000 restaurants and food service operators across the United States rely on US Foods as their national distributor for food and supplies. In return, US Foods takes a holistic approach to servicing customers by going beyond food offerings. The distributor provides a comprehensive suite of e-commerce technology and business solutions that help restaurants manage their entire business.

Connected Manufacturing’s Pivot to an Enterprise Data Solution Connected Manufacturing is at a turning point and it is catalyzed by a real, measurable change and shift in data types – real-time and time-series data is growing 50% faster than latent or static data forms and streaming analytics projected to grow at a 28% CAGR, leaving legacy data platforms that specialize in static historical data solutions, functioning on-prem or in discrete clouds, inadequate in addressing today’s rea

In Part 1 and Part 2 of this blog post series, Snowflake Service Account Security, discussed service accounts threats and how to mitigate those threats with Snowflake features. Part 3 demonstrates how to manage credential rotation with a sample Hashicorp Vault plugin. You can use many platforms to achieve similar results. The important thing is to understand the patterns used to apply these controls to protect your service accounts.

Today’s an exciting day in the world of data warehousing and analytics for enterprises that want to get more insight from their SAP data. Let’s face it – basically every enterprise on the planet that has SAP wants more value from their platform and data.

Today, we are introducing BigQuery Omni, a flexible, multi-cloud analytics solution that lets you cost-effectively access and securely analyze data across Google Cloud, Amazon Web Services (AWS), and Azure (coming soon), without leaving the familiar BigQuery user interface (UI). Using standard SQL and the same BigQuery APIs our customers love, you will be able to break down data silos and gain critical business insights from a single pane of glass.

“In today’s world of disruption and transformation, there are a few key things that all organizations are trying to figure out: how to remain relevant to their customer base, how to deal with the pressure of disruption in their industry and, undoubtedly, how to look to technology to help deliver a better service.” Paul Mackay Today we are sitting down with Marc Beierschoder, Analytics & Cognitive Offering Lead at Deloitte Germany and Paul Mackay, the EMEA Cloud Lead at Cloudera to dis

Learning additional languages is a common practice in the Netherlands. In primary school, we learn English and secondary school offers French, German, and a host of other options. Learning a new language and speaking it well is tricky.

As a BI Analyst, have you ever encountered a dashboard that wouldn’t refresh because other teams were using it? As a data scientist, have you ever had to wait 6 months before you could access the latest version of Spark? As an application architect, have you ever been asked to wait 12 weeks before you could get hardware to onboard a new application?

The COVID-19 pandemic is forcing every business to see the world differently. From examining business continuity plans, modernizing workforce plans, or building supply chain resiliency, no facet of business has gone untouched. As organizations combat the economic fallout now and in the coming years, agility has never been more important. The key to remaining agile is a better use of data.

This blogpost will cover how customers can migrate clusters and workloads to the new Cloudera Data Platform – Data Center 7.1 (CDP DC 7.1 onwards) plus highlights of this new release. CDP DC 7.1 is the on-premises version of Cloudera Data Platform.

The benefits of building a DevOps culture for software companies are clear. DevOps practices integrate once-siloed teams across the software development lifecycle, from Dev to QA to Ops, resulting in both faster innovation and improved product quality. As a result, most software development teams have deployed tools to enable DevOps practices across their workflow.

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.

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).

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.

In these uncertain times of the COVID-19 crisis, one thing is certain – data is key to decision making, now more than ever. And, the need for speed in getting access to data as it changes has only accelerated. It’s no wonder, then, that organisations are looking to technologies that help solve the problem of streaming data continuously, so they can run their businesses in real-time.

Research on COVID-19 is being produced at an accelerating rate, and machine intelligence could be crucial in helping the medical community find key information and insights. When I came across the COVID-19 Open Research Dataset (CORD-19), it contained about 57,000 scholarly articles. Just one month later, it has over 158,000 articles. If the clues to fighting COVID-19 lie in this vast repository of knowledge, how can Qlik help?

At Google Cloud, we work with organizations performing large-scale research projects. There are a few solutions we recommend to do this type of work, so that researchers can focus on what they do best—power novel treatments, personalized medicine, and advancements in pharmaceuticals.

We hear from our users in the scientific community that having the right technology foundation is essential. The ability to very quickly create entire clusters of genomics processing, where billing can be stopped once you have the results you need, is a powerful tool. It empowers the scientific community to spend more time doing their research and less time fighting for on-prem cluster time and configuring software.

In this release, we’ve focussed on providing exceptional capability for our customers - both enterprise and software companies - to create and deploy embedded analytics experiences that drive user adoption with minimum amount of effort and coding.

As first-party customer data continues to explode, companies have struggled to make it actionable for personalization, advanced analytics, and other business purposes. As a result, customer data platforms that consolidate and activate known customer information have emerged to help companies generate ROI from their data. At present, nearly 80% of marketing organizations already have a customer data platform or are developing one.