In the data landscape, the people are represented by two separate yet equally important groups. The data engineers who design the Lego blocks and the data scientists who build something extraordinary out of them. These are their stories. DUN DUN! And we’re back! Last time, we went over the toolkit needed to get your foot in the door as a data engineer. You’ve gotten over the first hurdle, but I hope you haven’t fallen prey to the Dunning-Kruger Effect.

A data warehouse is a centralized storage system for structured data. The data stored here is used for reporting, analytical processing and business intelligence.

A distributed database can offer benefits and tools to any company’s data storage and analytics processes — as long as they know how to use one.

Data science is vital to business success. It’s our window into the likes and habits of our customers, creating opportunities to glean insights from the mountains of data we collect every day. Data has always helped businesses with decision-making, but AI is taking it a step further. So much so that today it can even be applied to the practice of creating impressive email subject lines. Machine learning for information management is now a key ally for every organization worldwide.

Imagine you’re driving a car. You can see what’s happening on the road in front of you, but you have no idea what’s going on under the hood. It’s like driving blindly without any gauges or a dashboard to give you vital information. You don’t know how fast you’re going, how much fuel you have left, or if something is about to go wrong. In the same way, data engineers who lack data observability are like drivers with a limited view of the road.

Data is everywhere–driving the evolution of technology, changing the way we do business, transforming what it means to be a customer. Yet, too many businesses are still operating in a data-aware state and not truly adapting to a data-driven mentality. According to Deloitte Insights, just 1 in 10 executives believe that their employees can actually use data to make decisions.

ClearML is now officially integrated into the NVIDIA TAO Toolkit 🎉. For those of you that don’t know yet, the NVIDIA TAO Toolkit, built on TensorFlow and PyTorch, is a low-code version of the NVIDIA TAO framework that accelerates the model training process by abstracting away the AI/deep learning framework complexity.

Clouderans made a global impact by running a number of donation activities and local giving events to celebrate the season of giving.

We’re in the defining decade of data. Data underpins the technologies transforming how we work, communicate, socialize and buy. If you want to take part in the revolution, you need to become—or hire—a data leader. But what does that even mean? What sets data leaders apart from the average data-aware professional? And how can we become data leaders?

Across the energy supply chain from generation to consumer, we can see that the trend toward investing in renewable energy has picked up pace as demand has grown for energy companies to actively pursue investments in energies with little or no environmental impact in the quest for decarbonisation. McKinsey estimates that by 2035, 50% of energy will be wind and solar.

The ability to access, analyze and use information from multiple sources has been a key factor in modernizing manufacturing, resulting in more efficient processes, and improved economic success.

More than 1,000 connectors and components in Talend Cloud put powerful graphical tools at your fingertips to connect databases, big data sources, on-premises data, and cloud applications. Now imagine designing cloud-to-cloud and hybrid integration workflows in Talend Studio and automating publication to a fully managed cloud platform.

The ability to access, analyze and use information from multiple sources has been a key factor in modernizing manufacturing, resulting in more efficient processes, and improved economic success.

Our newest benchmark compares price, performance and differentiated features for Redshift, Snowflake, BigQuery, Databricks and Synapse.

While in the past, businesses used data to gain an edge over their rivals, in today’s competitive environment, data is imperative to stay in business. Modern businesses rely increasingly on data to manage all aspects of their operations, from everyday workflows to impacts on business strategy and customer interactions. As a result, data stacks have become extremely complex.

The query execution graph provides an intuitive interface with insights to help you understand, troubleshoot and optimize your BigQuery analytics queries.

Customers can solve marketing analytics, data governance and data sharing challenges with cross-cloud analytics.

Whether inflation, global conflict or supply chain disruption, 2022 has been full of significant events that have fundamentally impacted the business landscape. The undercurrent is an accelerating de-globalization process. But this isn’t just geo-political in nature; the re-distribution of power and fragmentation of data are inextricably linked. As a result, data-driven businesses are having to adapt to a more distributed world – fast.

Deliberations about defining your data approach often revolve around an offensive vs defensive data strategy. Here, an offensive strategy is focused on driving positive outcomes through increased revenues and profitability or by providing an enhanced customer experience. The primary objectives of an offensive data strategy are typically tailored to the product or business side of the organization, prioritizing AI and analytics use cases to drive superior commercial or financial outcomes.

The FIFA World Cup 2022 is nearing its end, and the final game promises to be a nail biter. What started with 32 countries battling it out for close to a month, will now culminate in a play-off between Argentina and France. FIFA projects more than 5 billion people to tune in for the tournament, perhaps making the World Cup Final 2022 the most watched event of the year!

We are pleased to announce that Cloudera has been named a Leader in the 2022 Gartner® Magic Quadrant™ for Cloud Database Management Systems. Cloudera has been recognized in this cloud DBMS report since its inception in 2020. This year we’ve been named a Leader. This validates our significant momentum in global enterprises. And together, with our recent recognition in the Gartner Peer Insights Customer Choice Distinction for Cloud DBMS, cements our position as an industry leader.

Late last June, I had the opportunity to attend a ThoughtSpot User Group session in London and share how ThoughtSpot has impacted global procurement at Roche—notably how it’s helped my team not become a dashboard factory for the rest of our business.

Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), Cloudera customers, such as Teranet, have built open lakehouses to future-proof their data platforms for all their analytical workloads. Cloudera partners are also benefiting from Apache Iceberg in CDP. For example, Modak Nabu is helping their enterprise customers accelerate data ingestion, curation, and consumption at petabyte scale.

Discover why businesses need data integration architecture, the factors that affect its structure and five best practices to implement while building it.

The quality of your data determines how well it supports your business goals within a given context, be it in operations, planning, or decision-making. Low-quality data cannot effectively serve your purpose. Usually, decision-makers rely on data to support their decisions; however, much evidence suggests that poor or uncertain data quality can contribute to ineffective decision-making in practice.

OCBC Bank is the second largest financial services group in Southeast Asia by assets and one of the most highly-rated banks in the world. Recognised for its financial strength and stability, OCBC Bank is consistently ranked among the World’s Top 50 Safest Banks by Global Finance.

Security and surveillance cameras are now almost ubiquitous in areas where people gather in numbers. From urban public spaces to sports grounds, shopping malls and offices, these cameras play critical roles in keeping people and property secure and protected, deterring crime and fraudulent activity, and helping to catch offenders. Founded in 2008 and now owned by Canon, BriefCam is a software provider committed to maximizing the value that video surveillance can deliver.

For the vast majority of US citizens, the front lines of conflict are witnessed from thousands of miles away on the nightly news. But for government agencies, these physical conflicts are the tip of the iceberg as cyberattacks persist as an underlying constant, inflicting enduring damage regardless of geopolitical tension or location.

Product owners enable data teams to serve your organization’s data needs. Learn to understand and measure their efforts.

What does it take to be successful in the world of data? We asked DJ Patil, one of the most influential data scientists in the world and guest keynote speaker at the 2023 Modern Data Stack Conference.

Over the past few months industry analysts have been making some pretty controversial recommendations for data management in the cloud. For a thoughtful and entertaining analysis, I strongly recommend you spend a few minutes watching the keynote session by Pat Moorhead, CEO Moor Insights & Strategy, at the Evolve 2022 Data event in New York. His takeaway: “The world is very much going to be hybrid and multi-cloud.” His delivery—priceless.

Many times, a business treats an engaged customer like a random prospect. Marketing systems may not correctly target with relevance, finance systems may apply discount rules across the board, or customer support systems may treat every client with the same indifference.

Cloudera has been providing enterprise support for Apache NiFi since 2015, helping hundreds of organizations take control of their data movement pipelines on premises and in the public cloud. Working with these organizations has taught us a lot about the needs of developers and administrators when it comes to developing new dataflows and supporting them in mission-critical production environments.

Joint customers can move faster with more visibility across their entire data stack, helping data teams collaborate better with increased trust in their data.

The growth of big data has scattered data sources throughout the enterprise, tucking them away in silos and making them hard to find. But there is more to data than just storing information in an operational data store or running analytics from a data warehouse. For example, regulated industries have requirements needing tighter control over the data.

In the past 10 years, the term ‘disruption’ has been abuzz across business and industry circles. Whether succeeding as a disruptive innovator or defending against a challenger as an incumbent, the nature of innovation has focused on the ability to re-imagine and execute business models. The nature of such business models has shifted the trajectory of demand either to totally non-existent markets or delivered from low-end footholds to scale.

You'll need to understand the six key components of a data pipeline and overcome five important technical challenges.

Whether on-premises, private, hybrid or multicloud, or at the edge – working in the cloud is complex. And as the enterprise expands, so too does the threat surface for cyberattacks. Ransomware, in particular, is among the biggest risks organizations now face and cloud-based data is accounting for 39% of successful attacks.

Make your customers comfortable opening their data to you with data pipelines that feel like they’re part of your product.

What an incredible week in Las Vegas for this year’s annual AWS re:Invent conference. The last time we were in-person for re:Invent pre-Covid, the show had grown to over 60,000 attendees. This year the energy and sheer volume of activity felt like we were right back there. The difference from 2019 in where the industry is, however, was readily on display throughout the week.

According to a McKinsey report, companies making intensive use of customer analytics are 2.6 times more likely to have a significantly higher ROI than their competitors. Intensive users of customer analytics are 23 times more likely to outperform their competitors in terms of new customer acquisition than non-intensive users, and 9 times more likely to surpass them in customer loyalty.

Cross-cloud analytics are critical to breaking down data silos. Moving data with usability, security, low-latency, and audibility makes it a reality.

When you’re racing around a circuit at over 200mph, there isn’t much room for error. There's a team of thousands backing you, millions of dollars on the line and a grid of other teams vying for the podium. In motorsport, every millisecond counts – and that’s where data can make the difference. This is what I spoke to Joe DosSantos about in the latest episode of Data Brilliant.

The data lakehouse is a promising new technology that combines aspects of data warehouses and data lakes.

Zeotap is a next-generation Customer Data Platform hosted in Google Cloud aimed at building customized Audience segments and activating at scale.

Use new BigQuery geospatial functions ST_IsClosed and ST_IsRing

Learn how Fivetran solution architects are supporting customers with implementing robust and scalable data platforms.

Full-stack observability is a critical requirement for effective modern data platforms to deliver the agile, flexible, and cost-effective environment organizations are looking for. For analytic applications to properly leverage a hybrid, multi-cloud ecosystem to support modern data architectures, data observability has become even more important. I spoke to Mark Ramsey of Ramsey International (RI) to dive deeper into that last subject.