Mercury, the Roman god of commerce, is often depicted carrying a purse, symbolic of business transactions, wearing winged sandals, illustrating his abilities to move at great speeds. Transactions power the world’s business systems today, ranging from millions of packages moving worldwide tracked in real time by logistics companies to global payments from personal loans to securities trading to intergovernmental transactions, keeping goods and services flowing worldwide.

In financial services, data is essential for storing product information, capturing customer details, processing transactions and keeping records of accounts; the relationship between products and their underlying data has always been symbiotic. A significant amount of data infrastructure is static, fragmented across data silos or based on legacy platforms. This has created an impedance mismatch between products and the underlying data.

June is the month which holds the summer solstice, and (at least in the northern hemisphere) we enjoy the longest days of sunshine out of the entire year. Just as the sun is making its longest trips across the sky, the BigQuery team is delighted to announce our next set of user-friendly SQL features.

When ATB Financial decided to migrate its vast SAP landscape to the cloud, the primary goal was to focus on things that matter to customers as opposed to IT infrastructure. Based in Alberta, Canada, ATB Financial serves over 800,000 customers through hundreds of branches as well as digital banking options. To keep pace with competition from large banks and FinTech startups and to meet the increasing 24/7 demands of customers, digital transformation was a must.

Data pipeline performance hinges on several important techniques, including algorithmic optimization and parallelization.

Today we’re announcing preview availability of a new public dataset for Google Trends. For the first time we’re bringing Google-owned Search data into Google Cloud Datasets for convenient analysis in BigQuery, or through your favorite business intelligence tools.

Google’s cloud data warehouse, BigQuery, has enabled organizations around the world to accelerate their digital transformation and empower their data analysts to unlock actionable insights from their data. Using BigQuery ML, data analysts are able to create sophisticated machine learning models with just SQL and uncover predictive insights from their data much faster.

In today’s digital age, keeping up with market trends is exactly what a business has to do to stay ahead. Creating a solid online brand image plays a key role in this task, and to do it, dedicated SEO efforts go a long way. Crafting targeted keywords that can direct traffic to your webpages can work wonders in capturing a widespread customer base. Now what if we were to tell you that instead of doing everything manually, you could rely on an automated tool to take care of things?

Two data experts share their journeys toward better customer engagement, fueled by faster access to more data.

Starting this week, we’re adding new content to the BigQuery Spotlight Youtube series. Throughout the summer we’ll be adding new videos and blog posts focused on helping new BigQuery architects and administrators master the fundamentals. You can find complimentary material for the topics discussed in the official BigQuery documentation.

Many Cloudera customers are making the transition from being completely on-prem to cloud by either backing up their data in the cloud, or running multi-functional analytics on CDP Public cloud in AWS or Azure. The Replication Manager service facilitates both disaster recovery and data migration across different environments.

Best-in-class database replication now combines the benefits of snapshots and log-based systems — while avoiding the drawbacks.

CDP Operational Database Experience (COD) is a PaaS offering on the Cloudera Data Platform (CDP). COD enables you to create a new operational database with a few clicks and auto-scales based on your workload. Behind the scenes, COD automatically manages cluster deployment and configuration, reducing overheads related to setting up new database instances. Additionally, auto-scaling eliminates the need to size a cluster for your workloads.

Climate change is a challenge for insurers in some obvious ways, such as stronger and more frequent natural disasters. Yet there are also more subtle risks to monitor, including changes to insured assets, risks, and exposures. Climate impacts the production quality and quantity of insured consumable goods, their location, and their supply chains.

Not a day goes by without questions from candidates, customers, and other interested parties about how we run Snowflake Engineering. I often hear: “Snowflake has been delivering a truly innovative, high quality product, and the pace of delivery is only accelerating. There must be a secret to it.” Indeed, we have a unique engineering team, and continue to hire world-class engineers. They are the driving force behind our products.

If you’ve been working with SEO for some time, you’d know that although it’s a reliable way of improving your website’s searchability, it’s often marred by the downside of being very time-consuming. Moreover, manual SEO strategies could easily fail to achieve the desired results because of the ever-increasing level of competition. In such a scenario, an automated tool that helps you enhance your SEO efforts can prove to be a boon.

Across organizations large and small, ML teams are still faced with data silos that slow down or halt innovation. Read on to learn about how enterprises are tackling these challenges, by integrating with any data types to create a single end-to-end pipeline and rapidly run AI/ML with Azure Synapse Analytics with Iguazio.

Data integration enables machine learning. Learn how you can use video analytics to automatically detect patterns in video.

Three leading data practitioners share advice on building a modern analytics practice — including how to avoid costly missteps.

Data security is an ongoing concern for anyone managing a data warehouse. Organizations need to control access to data, down to the granular level, for secure access to data both internally and externally. With the complexity of data platforms increasing day by day, it's become even more critical to identify and monitor access to sensitive data.

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

At Parameta Solutions, clients have come to expect our data, and our data products, to be robust and reliable. The way we really stand out in this specialist arena is through the quality and sophistication of our client services. I was delighted recently to share my experiences of how ThoughtSpot is supporting us in both of these aims during a webinar hosted by Cindi Howson during the Chief Data & Analytics Officers UK, 2021 event last February.

In the second of this two-part episode of Data+AI Battlescars, Sandeep Uttamchandani, Unravel Data’s CDO, speaks with Kapil Surlaker, VP of Engineering and Head of Data at LinkedIn. In part one, they covered LinkedIn’s challenges related to metadata management and data access APIs. This second part dives deep into data quality.

In the first of this two-part episode of Data+AI Battlescars, Sandeep Uttamchandani, Unravel Data’s CDO, speaks with Kapil Surlaker, VP of Engineering and Head of Data at LinkedIn. In this first part, they cover LinkedIn’s challenges related to Metadata Management and Data Access APIs. Part 2 will dive deep into data quality.

Build a comprehensive picture of how customers interact with your company.

We’re all familiar with how Google Search revolutionized information processing for consumers. The ingenious combination of AI with a new way to organize content on the web created a user-friendly experience that forever changed how the world finds relevant information on the internet.

The pandemic upended business for many or at the very least cast a grim shade of uncertainty, so, as many took to working from home, they also were commissioned with cutting waste. Among the biggest sources of misspend in 2020 – cloud services. And remote work may have actually spurred the problem, as organizations migrate more applications to the cloud to support these workers.

In this episode of Data+AI Battlescars (formerly CDO Battlescars), Sandeep Uttamchandani talks to Chu-Cheng, CDO at Etsy. This episode focuses on Chu-Cheng’s battlescars related to recruiting and building a data science team. Chu-Cheng leads the global data organization at Etsy. He’s responsible for data science, AI innovation, machine learning and data infrastructure. Prior to Etsy, Chu-Cheng has led various data roles, including at Amazon, Intuit, Rakuten and eBay.

At Cloudera, we have long believed that automation is key to delivering secure, ready-to-use, and well-configured platforms. Hence, we were pleased to announce the public release of Ansible-based automation to deploy CDP Private Cloud Base. By automating cluster deployment this way, you reduce the risk of misconfiguration, promote consistent deployments across multiple clusters in your environment, and help to deliver business value more quickly.

At Snowflake Summit 2021, we announced that Snowpark and Java functions were starting to roll out to customers. Today we’re happy to announce that these features are available in preview to all customers on AWS today. These features represent a major new foray into data programmability, enabling you to more easily make Snowflake’s platform do more for you. Snowflake started its journey to the Data Cloud by completely rethinking the world of data warehousing to accommodate big data.

What is MongoDB used for? Learn how MongoDB and its Fivetran connector can help you.

Speed and agility are vital in today’s dynamic economy. But moving fast in the dark is dangerous. Decision makers need the insights and guidance they can only get from reliable data. But despite massive efforts from their internal BI teams, getting that data when and how they need it has been problematic.

For more than 20 years, dashboards served as a foundational element of business intelligence, helping leaders visualize and share valuable data across their organization. At inception, dashboards were the perfect vehicle for delivering key report KPIs without data workers needing a background in coding or IT. But much has changed over the last two decades, including the appetite and needs of your business users.

As the inexorable drive to cloud continues, telecommunications service providers (CSPs) around the world – often laggards in adopting disruptive technologies – are embracing virtualization. Not only that, but service providers have been deploying their own clouds, some developing IaaS offerings, and partnering with cloud native content providers like Netflix and Spotify to enhance core telco bundles.

What is NoSQL, and what does it bring to the table?

BigQuery Reservations help manage your BigQuery workloads. With flat-rate pricing, you can purchase BigQuery slot commitments in 100-slot increments in either flex, monthly, or yearly plans instead of paying for queries on demand. You can then create/manage buckets of slots called reservations and assign projects, folders, or organizations to use the slots in these reservations. By default, queries running in a reservation automatically use idle slots from other reservations.

Help your team develop the appropriate skills to win in the age of data-driven marketing.

Three thought leaders explore how cloud technology is reshaping data architecture and revolutionizing analytics.

Regardless of how technically sound the engineering of an app is, bugs, errors, and crashes can happen. So when they do, you must recover from it by doing a deep analysis of the technical aspects and the impact on the overall customer experience. If your crypto-exchange or banking app is not getting the right insights you need from the crashes, your churn rate and your customers will definitely let you know sooner than you think.

It’s no secret that what companies need from their data and what they can actually get from their data are two very different things. According to our recent survey, most executives work with data every day, but only 40% of them always trust the data they work with. We also discovered that 78% of them have challenges making data-driven decisions. Virtually every business is collecting more data than ever before, so lack of data can’t be the issue.

Apache Spark is a very popular analytics engine used for large-scale data processing. It is widely used for many big data applications and use cases. CDP Operational Database Experience Experience (COD) is a CDP Public Cloud service that lets you create and manage operational database instances and it is powered by Apache HBase and Apache Phoenix.

As a product leader, there’s no better way to show the value of your API platform then by graphically displaying key metics. If you’re already working with Tableau, it’s easy to extract key charts, or workspaces, from Moesif’s dashboards and insert them into your visualization platform. The information from Moesif will be inserted into Tableau as a web page object.

Address enterprise security requirements with customer-managed keys, AWS PrivateLink and additional compliance certifications.

Some popular data integration tools compared.

If you have read our previous post focusing on the challenges of planning, launching and scaling IIOT use cases, you’ve narrowed down the business problems you’re trying to solve, and you have a plan that is both created by the implementation team and supported by executive management. Here’s a plan to make sure you’ve got it all down. Think of these success factors like the legs of a kitchen table and the results that you desire, a bowl of homemade chicken soup.

At this year’s Snowflake Data Cloud Summit: Data Together Now, customers and partners from around the world came together to explore the transformational power of data and Snowflake’s vision for bringing the world’s data together in the Data Cloud. Over the course of two days and 70 sessions, attendees were inspired by keynotes, learned from their peers in customer and partner sessions, and participated in hand-on labs and technical deep-dives.

In a telecoms industry differentiated mainly by service and threatened by churn, gaining access to timely customer data initially drove internal demand for analytics and BI (ABI) reporting. When demand grew beyond customer service, our IT team really struggled to keep up. This set us off on a journey to find ways to help our colleagues to find insights and the answers to their questions themselves.

When dealing with payments, speed is king. Technology is increasing the speed at which innovation happens, and nowhere is that more apparent than how everyday commerce is transacted. Credit cards are now built with tap-to-pay capabilities, if you still even have a physical credit card. Phones have become the new credit card, with data from transactions and mobile banking adding to the overwhelming amount of data that can be collected.

At the end of March, we released the first version of Cloudera SQL StreamBuilder as part of CSA 1.3. It enabled users to easily write, run and manage real-time SQL queries on streams from Apache Kafka with an exceptionally smooth user experience. Since then, we have been working hard to expose the full power of Apache Flink SQL and the existing Data Warehousing tools in CDP to combine it into a state-of-the-art real-time analytics platform.

Cloudera has been named as a Strong Performer in the Forrester Wave for Streaming Analytics, Q2 2021. We are excited to be recognized in this wave at, what we consider to be, such a strong position. We are proud to have been named as one of “The 14 providers that matter most” in streaming analytics. The report states that richness of analytics, development tool options and near-effortless scalability are what streaming analytics customers should look for in a provider.

In October of 2020 Cloudera acquired Eventador and Cloudera Streaming Analytics (CSA) 1.3.0 was released early in 2021. It was the first release to incorporate SQL Stream Builder (SSB) from the acquisition, and brought rich SQL processing to the already robust Apache Flink offering. The team’s focus turned to bringing Flink Data Definition Language (DDL) and the batch interface into SSB with that completed.

The thing about data is there’s no end to how much of it you can collect and keep. Each day, if you’re like most of the global banks I work with, you’re generating oceans of the stuff. Yet the bulk of it will never be very helpful or even relevant to your day-to-day business decisions.

Marketing analytics enables personalization, but what other benefits does it offer — and how relevant are they to your department?

Every marketer knows the old John Wanamaker quote, “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half.” With the introduction and evolution of multi-touch attribution (MTA), the media industry has moved closer than ever to understanding the impact of each touchpoint on a customer’s behavior, from signing up for a newsletter to making a purchase.

Make sure your data integration solution can connect to critical data sources.

Cloudera Support’s cluster validations proactively identify known problem signatures contained in customers’ diagnostic data with the goal of increasing cluster health, performance, and overall stability. Cluster validations are included in a customer’s enterprise subscription at no additional cost. All customers with access to the Support case portal will also be able to take advantage of cluster validations.

Last year, Harvard Business Review and ThoughtSpot published a groundbreaking survey on the business benefits of empowering frontline workers with data. Revenues are higher, operations more efficient, customer service better, and employees happier. And yet, few organizations deploy BI this way, historically held back by the technology, conflicting priorities, and mindset.

I’m extremely excited to introduce Continual. Continual is the easiest way to maintain predictions – from customer churn to inventory forecasts – directly in your cloud data warehouse. It’s built for modern data teams that want to leverage machine learning to drive revenue, streamline operations, and power innovative products and services without complex engineering.

Data pipelines are in high demand in today’s data-driven organizations. As critical elements in supplying trusted, curated, and usable data for end-to-end analytic and machine learning workflows, the role of data pipelines is becoming indispensable. To keep up, data pipelines are being vigorously reshaped with modern tools and techniques.

Apache Ozone is a distributed object store built on top of Hadoop Distributed Data Store service. It can manage billions of small and large files that are difficult to handle by other distributed file systems. As an important part of achieving better scalability, Ozone separates the metadata management among different services: Ozone Manager (OM) service manages the metadata of the namespace such as volume, bucket and keys.