Recently there has been a lot of excitement around the idea of a stand-alone metrics layer in the modern data stack. Traditionally, metrics have been defined in the BI or analytics layer where various dashboards are used to look at business metrics like Revenue, Sales Pipeline, numbers of Claims, or User Activity. Given that most organizations end up with multiple BI/Analytics tools, the idea has a lot of merits.

Google Cloud has a genuine passion for solving technology problems that make a difference for our customers. With the release of our BigQuery Connector for SAP, we're taking a another big step towards solving a major challenge for SAP customers with a quick, easy, and inexpensive way to integrate SAP data with BigQuery, our serverless, highly scalable, and cost-effective multi cloud data warehouse designed for business agility.

Read any overview of how the finance and accounting function is changing, and you will notice several common themes. Three of the most important of these are: cloud migration, data standardization, and interoperability. The aim of technology in finance is to remove friction. With cloud migration that means making upgrades, licensing, procurement and maintenance simpler with software-as-a-service (SaaS) models.

If a tree falls in a forest and there’s no one to hear it… well, it's very hard to use that tree in your corporate decision-making. KPIs (key performance indicators) are the metrics that tell you how well you're performing against certain key strategic targets. Ongoing KPI reporting gives you a clear and consistent view into your company’s performance in the areas that matter most.

The word “trust” is simply not in the cybersecurity solution providers’ vocabulary. They trust no one and nothing. They develop Zero Trust network architectures to protect clients against external attacks, insider threats, and inadvertent errors that could impact security. And they’re always on the lookout for emerging vulnerabilities across internal and external networks. Trust is a luxury they can’t afford to have — or can they?

As I meet with our customers, there are always a range of discussions regarding the use of the cloud for financial services data and analytics. Customers vary widely on the topic of public cloud – what data sources, what use cases are right for public cloud deployments – beyond sandbox, experimentation efforts. Private cloud continues to gain traction with firms realizing the benefits of greater flexibility and dynamic scalability.

Today we’re excited to announce the general availability of the Snowpark API for Scala and Java UDFs on AWS. Snowpark is the developer framework for Snowflake, bringing deep, language-integrated data programmability to users in the languages they love. At its core, Snowpark is all about extensibility.

During the holiday break, our family had to get tested for COVID. We got at-home tests, as they were available, and it was easier than having to wait in line at a testing center. This got me thinking of a recent article in the New York Times, which raises the notion that experts are questioning the importance/value of measuring COVID caseloads with the rise in the use of at home tests.

A lot of things got easier in 2021 as technology advanced tremendously to continue making the digital world more accessible and integrated into our everyday lives. But luckily there was something that got harder in 2021: being irresponsible with data. You see, with great power comes great responsibility, and as people’s data gets poured online, more and more restrictions appear to ensure that data is always safe, without exceptions.

Create and maintain dev, test and prod modern data stack deployments with Fivetran’s Terraform provider.

According to 451 Research, 96% of enterprises are actively pursuing a hybrid IT strategy. Modern, real-time businesses require accelerated cycles of innovation that are expensive and difficult to maintain with legacy data platforms. Cloud technologies and respective service providers have evolved solutions to address these challenges.

Digital transformations are fueling the equivalent of a global data gold rush. We know from leading analyst firms that 90% of all data was created in the last two years and it will grow by a factor of two over the next five years. This has resulted in a changed perception of business, one that requires a shift from conventional business models to those that leverage data to gain actionable insights to improve operations, enhances customer experience and accelerates revenue.

Data has long been a critical asset for businesses like yours to understand customers, operate more efficiently, inform go-to-market strategies, and retain your best employees. In a digital world, capturing and creating data-driven insights provides a major competitive advantage for those who can turn insights into action.

Data has long been a critical asset for businesses like yours to understand customers, operate more efficiently, inform go-to-market strategies, and retain your best employees. In a digital world, capturing and creating data-driven insights provides a major competitive advantage for those who can turn insights into action.

Data governance is a complex topic. In a nutshell, it refers to the aspect of data management concerning an organization's ability to ensure (A) that high data quality exists throughout the complete data lifecycle, and (B) that sufficient data controls are in place to support business objectives. In practice, data governance is the collection of processes, roles, policies, and standards that ensure a balance between access and control for information throughout an organization.

In part one and two, we introduced Iguazio's feature store and discussed the benefits of using one in the ML workflow. Additionally, we ingested and transformed the data that we will be using to train our model. In this blog, we will do the following.

The start of a new year is a perfect time to reflect on what was accomplished and look forward, re-evaluate what we can do better. Change, although difficult at first, can also be very rewarding. That’s why I was excited to see similar sentiments shared at Thoughtspot beyond.2021 to move beyond the traditional dashboards of the past.

The Data Mesh is a fascinating approach to designing and developing data architectures – and it’s generating a lot of attention and discussion in the data world. The concept, introduced by Zhamak Dehghani, challenges the status quo idea of a centralized, monolithic data architecture. Instead, advocating for a decentralized domain-driven design.

For Snowflake CIO and CDO, Sunny Bedi, “life after dashboards” isn’t just a catchy tagline – it’s how he runs his business. At the helm of Snowflake’s 250-person IT and security organization, Bedi oversees a range of strategic initiatives including security and access control, data quality, and general system availability and performance.

Dozens of new connectors, partner integrations, security and compliance enhancements — and the Fivetran data models you love.

Organizations increasingly turn to AI to transform work processes, but this rapid adoption of models has amplified the need for explainable AI. Explaining AI helps us understand how and why models make predictions. For example, a financial institution might wish to use an AI model to automatically flag credit card transactions for fraudulent activity. While an accurate fraud model would be a first step, accuracy alone isn’t sufficient.

Co-founded by a professional tennis player stricken with cancer at age 24 and his wife, Your Super is an eCommerce company focused on helping people improve their health with nutritious superfoods. Even in its early days, the company focused on using their data to uncover customer wants and help them get the most value out of their purchases. “When I first started, the company was maybe 20 people,” explains Gabriel Freeman, Business Intelligence Manager for Your Super.

Let’s start with a hard truth: If you try to do everything, you won’t excel at anything. In a growing business, there’s no shortage of things that need attention, but you can’t do everything at once. Instead, you have to decide where to focus your resources to get the greatest impact. In a word, you must become a master of triage.

Most enterprises that have embarked on their digital business journey have quickly realized that data is the foundation of every digital business. No successful digital business has succeeded without a data strategy. So, when I joined Joe DosSantos, Chief Data Officer at Qlik, on the Data Brilliant podcast, we discussed how data has been democratized, why certain organizations have succeeded with digital transformation, and what factors are required to build the culture for success.

The concept that data is critical to an organization's growth is nothing new. As digital transformation takes every industry by storm, however, the types and sources of data have rapidly evolved. Relying simply on proprietary data is blinding at best. Over the last few years, innovative companies have raced to tap into new sources — often those they don’t own.

This blog will summarise the security architecture of a CDP Private Cloud Base cluster. The architecture reflects the four pillars of security engineering best practice, Perimeter, Data, Access and Visibility. The release of CDP Private Cloud Base has seen a number of significant enhancements to the security architecture including: Before diving into the technologies it is worth becoming familiar with the key security principle of a layered approach that facilitates defense in depth.

The Snowflake Data Cloud is a powerful place to work with data because we have made it easy to do difficult things with data, such as breaking down data silos, safely sharing complex data sets, and querying massive amounts of data. As customers move to the Data Cloud, their needs and timelines vary—our goal is to meet every customer where they are on their Data Cloud journey.

Column masking for PII and other sensitive data is now universally available — including for SaaS apps, files, events and more.

AI models get smarter, more accurate, and therefore more useful over the course of their training on large datasets that have been painstakingly curated, often over a period of years. But in real-world applications, datasets start small. To design a new drug, for instance, researchers start by testing a compound and need to use the power of AI to predict the best possible permutation.

In 2020, ThoughtSpot and Harvard Business Review Analytic Services teamed up on original research demonstrating the power of the new decision-makers — employees on the frontlines of your business who interact with outside customers, partners, and vendors on a daily basis. Through our research, we found that 87% of leaders said their organization will be more successful when frontline workers are empowered to make important decisions in the moment. Why?

Easily combine engagement metrics from Facebook, Instagram, Twitter and LinkedIn.

Last time, we discussed why organizations might require the functionality of a feature store like Iguazio's. In this blog, we will actually get into the project and cover the following.

We are excited to announce the release of Cloudera Streaming Analytics (CSA) 1.6 for CDP Private Cloud Base. With this release, we build on the foundation on 1.4 and 1.5 – with a number of fixes, enhancements, and features. Starting with this release, we now have an aligned release cycle for CSA Community Edition (CE). You can now expect simultaneous releases of CSA for both CE and CDP Private Cloud Base versions.

Gartner® recognized Cloudera in three recent reports – Magic Quadrant™ for Cloud Database Management Systems (DBMS), Critical Capabilities for Cloud Database Management Systems for Analytical Use Cases and Critical Capabilities for Cloud Database Management Systems for Operational Use Cases.

Organizations with the best Artificial intelligence (AI) capabilities are poised to deliver better customer outcomes, maximize revenue opportunities, and leave the competition behind. AI-driven insights can help companies achieve many business objectives such as improving customer loyalty, delivering effective training, reducing the impact of outages or providing better healthcare to patients. Applications are aplenty.

Founded in 1934 as a postal and telephone betting service, William Hill was historically synonymous with the world of horse racing. Since then, the company has continued to grow into other sporting avenues, with the retail arm—which opened in 1966—now amounting to more than 1,400 shops. Fully embracing the potential of the digital revolution, the company now comprises over 10,000 employees over six brands, operating through offices in ten countries.

New Fivetran features help data teams jointly orchestrate Fivetran connectors and transformations using dbt Labs’ open-sourced dbt Core* to minimize data latency, monitor ELT pipelines in real time and reduce engineering time.

ThoughtSpot provides a rich suite of APIs for search, user controls, and platform metadata. These APIs can be especially useful for sharing data with partners via webhooks, building native mobile apps, providing results in json format for third-party charting libraries like D3, or scripting ThoughtSpot configuration to support devops and continuous integration toolchains.

As every traveler knows, even the best-laid plans sometimes go astray. Stuff happens. Trains are delayed. Storms cause outages. People forget their bags. A special event creates long lines. Transportation providers can’t take the uncertainty out of travel or make the world operate precisely on schedule — particularly during a global pandemic.

The final month of 2021 brought new capabilities for data governance, expanded insights for Snowflake Data Marketplace providers, and several new providers added to the Marketplace. For the full list, see the Release Notes.

Market disruptions over the past two years have driven a global surge of mergers and acquisitions. According to Ernst & Young, the first six months of 2021 saw a record $2.6 trillion in M&A activity, obliterating normal levels that hovered around $1.5 trillion per six months prior to the pandemic.

Learn how data integration is the key to a mature analytics practice and more

As anyone running modern data applications in the cloud knows, costs can mushroom out of control very quickly and easily. Getting these costs under control is really all about not spending more than you have to. Unfortunately, the common approach to managing these expenses—which looks at things only at an aggregated infrastructure level—helps control only about 5% of your cloud spend.

The 2021 holiday season is over, and, with a bump we’ve landed in 2022, with something of a repetitive tune playing. The COVID pandemic continues; we are a full two years post the first cases being reported; and the statistics are no less shocking today than they were at the outset.

When companies realized data was a resource and not just the by-product of sales, they began to offer customers additional value: free products, savings, money back, even a club-like exclusivity. They often did it, and still do it, with customer loyalty programs, which were frequently expressed in the form of a physical card, such as Starbucks Rewards and MyPanera. But the very biggest companies have changed the loyalty game.

I have written many a post about the importance of keeping your data moving. As Mathew Wilder sang in “Break My Stride,” it’s “got to keep on moving” to enable you to act within the business moment. Real-time data analytic pipelines are the solid foundation to achieving the state of Active Intelligence, and you can only start that when you work on the freshest data available to you.

Bayer Crop Science uses Google Cloud to analyze billions of acres of land to better understand the characteristics of the soil that produces our food crops. Bayer’s teams of data scientists are leveraging services from across Google Cloud to load, store, analyze, and visualize geospatial data to develop unique business insights. And because much of this important work is done using publicly-available data, you can too!

In this series of blog posts, we will showcase an end-to-end hybrid cloud ML workflow using the Iguazio MLOps Platform & Feature Store combined with Azure ML. This blog will be more of an overview of the solution and the types of problems it solves, while the next parts will be a technical deep dive into each step of the process.

With hundreds of SaaS products in a given company, it’s getting more and more challenging and time-consuming to manage supplier relationships and negotiate contracts. Vendr has completely transformed this entire process by creating a SaaS buying platform where stakeholders can easily handle the relationships and contracts of their different software suppliers in a centralized platform.

Last year, we discussed how 2020 had been a year of adaptation, both to the new business challenges that came about by the pandemic and to the behavioral changes in all individuals. In 2021, adaptation gave way to adoption: of new technologies to better cope with business needs, of data-driven processes that guide product development, and guidelines for digital-first collaboration across teams with varying degrees of remote availability. What is there to 2022 that will make our heads turn?

The Fivetran data models for Shopify Holistic Reporting easily combines Shopify and Klaviyo data into analytic-ready models, helping analysts create a rich portrait of customer personas for marketing and much more.

If you’ve ever been on a long road trip, then you know the feeling of relief that comes when finally spy a recognizable truck stop or service station to pull into. Whether to fill up the tank, grab a snack, or ask for directions, fuel stations are a ubiquitous part of automotive travel, and there are more than 150,000 of them across the country. Pilot Flying J accounts for 750 of those locations, serving more than 1.3 million people daily.

Dataflow automation platform Prefect brings Fivetran connectors to its open-source orchestration project.

When AI came on the scene in analytics, Qlik took a very different approach than ‘black box’ tools. We felt strongly that AI should be utilized to enhance human intuition, instead of replacing it. And of course, the term augmented analytics is now well known. That’s not a coincidence.

Today we’re announcing a public preview for the BigQuery native JSON data type, a capability which brings support for storing and analyzing semi-structured data in BigQuery. With this new JSON storage type and advanced JSON features like JSON dot notation support, adaptable data type changes, and new JSON functions, semi-structured data in BigQuery is now intuitive to use and query in its native format.

Most data science projects don’t pass the PoC phase and hence never generate any business value. In 2019, Gartner estimated that “through 2022, only 20% of analytic insights will deliver business outcomes”. One of the main reasons for this is undoubtedly that data scientists often lack a clear vision of how to deploy their solutions into production, how to integrate them with existing systems and workflows and how to operate and maintain them.

The start of a new year is a great time for reflection, and, when I look at the progress organizations are making on their data journey, I’m feeling quite positive as we welcome 2022.

Growing your eCommerce business means you have to know everything there is to know about your customers and your business website. Analytics tools can help you market to the right people, learn about problems with your business, and help you earn more revenue. Here are the best reporting tools for your eCommerce website.