Keep track of new releases for our data connectors and data models for dbt with this regularly updated list.

With the recent announcement of ThoughtSpot Sage, we launched a number of enhancements to our search capabilities including AI-generated answers, AI-powered search suggestions, and AI-assisted data modeling. In this article we will walk you through the steps we take to secure your data during the LLM interaction.

The explosive rise of generative AI has prompted incredible excitement about its transformative potential, much like the advent of the Internet. But if like me, you’re old enough to remember what that looked like circa 1995, there was a lot we did not know at the time, creating uncertainty in both worlds of work and education on how to best leverage it, and whether providing unlimited access to employees or students was a good idea.

The first secure, enterprise-grade generative AI platform We have an exciting announcement! On Thursday, May 18, we released ClearGPT, the first generative AI platform that transcends enterprise ChatGPT challenges. ClearGPT is the only secure, enterprise-grade platform offering state-of-the-art LLMs, tailored to your business data and running securely on your network, to power enterprise AI transformation. Prefer to watch the 2-minute video and see how it works? Watch now.

Riding the wave of the generative AI revolution, third party large language model (LLM) services like ChatGPT and Bard have swiftly emerged as the talk of the town, converting AI skeptics to evangelists and transforming the way we interact with technology. For proof of this megatrend look no further than the instant success of ChatGPT, where it set the record for the fastest-growing user base, reaching 100 million users in just 2 months after its launch.

In a world where user experience and IT support can mean the difference between hitting or missing your ARR marks, businesses have to find smarter ways to build workflows and support their IT departments. That’s where companies like ServiceNow come into play. A few years back, we created our ServiceNow SpotApp, a pre-built analytics template to help companies analyze and understand their data—so they can increase efficiencies across their complex IT environments.

There are millions of data products out there, some successful and others…less so. But the truly standout data products are the ones that change users’ behavior. You know you’ve built something special when your users start forming habits around your product. The question is, how do you create something that stands out in a sea of data products? We believe it comes down to one thing: a relentless focus on delivering user value.

Your teams need constant connectivity to your organization's data and systems to operate effectively and thrive. And with digital transformation spurring a more rapid pace of innovation and technology adoption, this need for connected, democratized data is on the rise. That makes data silos an enemy you can no longer tolerate. Data fabric is the modern answer for eliminating data silos. Data silos occur when data is stored in separate systems or departments without proper integration.

How to chart a roadmap to the pinnacle of data science.

In my last article, I outlined how we in Snowflake Support use contextual data about where our customers get stuck to improve the overall product experience. Now I’ll take you through how your organization can also implement these important feedback loops from support to product enhancements, to your company’s—and your user’s—benefit. Customers don’t wake up in the morning and decide they’d love to spend time with a Support team.

Since leaving university, I have always been involved with data, learning so much during my time as an automation engineer and big data engineer working for an automotive tier 1 manufacturing company and as a data architect for an IT consultancy in the SAP and BI domain. And today, I count myself very fortunate to have a job at Kensu that I am truly passionate about. As a Technical Solution Architect, I get to help organizations with data health and data engineering problems on a daily basis!

Last week’s announcement of a data fabric approach from Microsoft was interesting to us on a number of levels, most notably for what it confirmed: Data Fabrics are now mainstream. If you’ve not heard why Data Fabrics are the next big thing, then here is a bit of history. Data Fabric is a concept that started to take hold over a decade ago with published research from Forrester.

In today's fast-paced and ever-evolving business landscape, organizations are constantly striving to gain a competitive edge, make well-informed decisions, and fuel their growth. As a result, advanced Business Intelligence (BI) tools have emerged, revolutionizing the way businesses harness the power of data to drive their success. These sophisticated BI tools offer a wide range of capabilities, empowering companies to collect, analyze, and visualize data from various sources.

Bulk data exports in the Google Search console let you export search data to BigQuery and run complex queries and create custom reports on it.

BigQuery object tables offer a structured record interface for unstructured data, so you can process and manage it securely and programmatically.

The BigQuery partitioning and clustering recommender analyzes workloads and tables and identifies potential cost-optimization opportunities.

A continuation of our series on ASCII files; how to ingest data from HL7 files.

Learn how to overcome the complexity of SAP systems to leverage centralized manufacturing data.

Have you ever been in a meeting and wondered how something is calculated? Or what another department’s acronym means? Or how that topic is actually defined? In some moments, you don’t need the data itself — you just need the definition of a data term. In that situation, you want the ability to look up a term in a business glossary (or data glossary) as quickly and simply as possible.

DBS Bank Head of Automation, Infrastructure for DBS Big Data, AI and Analytics Luis Carlos Cruz Huertas has a 1-on-1 discussion with Unravel CEO and Co-founder Kunal Agarwal about the convergence of DataOps and FinOps. The discussion, Leading Cultural Change for Data Efficiency, Agility, and Cost Optimization, was held at a recent Untap event in New York and revolves around best practices, lessons learned, and insights on.

After 30 years of working in tech across Asia, I’ve seen a lot of ups and downs in these markets. Not long ago, I wrote a blog about what US and European software companies can do to ensure their success when opening for business in the region.

By now, you would have read the headlines that Qlik's acquisition of Talend is complete, and we're excited to expand our best-in-class capabilities to help you access, transform, trust, analyze, and take action with your data. You might have seen Mike Capone's QlikWorld keynote or the recent "What's Next is Now" webinar and wondered how to leverage these new capabilities in your organization.

It’s that time of the year again! I’m still buzzing about this year’s Showfloor Showdown at Gartner Data & Analytics Summit in London, where I had the opportunity to showcase ThoughtSpot's AI-Powered Analytics. In the spirit of facilitating a side-by-side comparison, we were all invited to look at global flooding and weather station data, analyze the variables affecting these natural disasters, and present key findings to the crowd.

Data asset standardization is the purposeful and carefully planned consolidation of redundant, contradictory reports, processes, and databases into enterprise standards.

Using data to make decisions is actually really hard. Yet nearly every tech company, business, and team touts “data-driven” as the de-facto way that they operate. In practice, this intention to be data-led is often just aspirational—only about a quarter of organizations report that they are actually data-driven, according to Harvard Business Review. It’s easy to see why organizations strive to more effectively use their data.

Look for the following must-haves when you want to move data seamlessly between sources and destinations of all kinds.

The scope of mobile devices has reached beyond the realms of standard smartphones and tablets, venturing into the exciting worlds of wearable technology and IoT devices. ‍ As the landscape of mobile tech expands, so does the significance of mobile analytics. Here at Countly, we understand the power of these analytics to provide businesses with a lens into user experience, fostering data-driven decision-making and allowing us to fine-tune our offerings continually.

Data practitioners should bring best practices from BI, especially automated data integration, to data science.

The shift from single public cloud architectures to hybrid multi-cloud continues at enterprises around the globe, as organizations look for greater control, lower costs, and improved performance. I had the opportunity recently to speak with Vaughn Eisler, director of business development at Equinix, about this massive shift and some of the complex reasons behind them. Eisler sees several reasons for the movement.

New insightsoftware Platform connects financial analytics, reporting, and performance management solutions to share data across applications, solve business problems faster, and do more with less.

Kafka adoption is growing fast. Very fast. At Lenses, we’re pushing out new features to increase developer productivity, reduce manual effort & improve the cost & hygiene of operating your Kafka platform. Only a few weeks since Lenses 5.1, yet here we are again with more goodies in our release 5.2.

A continuation of our series on ASCII files; how to ingest data from EDI files.

As CEO of Qlik, I have never been more excited about the market, our strategic vision, and the value we bring customers all around the world. We have always been focused on looking forward and providing the most comprehensive and trusted solutions to our customers. Now, with Talend's cloud data integration and data quality solutions, we can offer even more.

Features like prebuilt data models, version control and integrated scheduling make data transformation faster and more powerful.

During QlikWorld ’23 in Las Vegas, we were thrilled to announce the general availability of Data Console in Talend Data Inventory. With data-driven decision making becoming more crucial for organizations, it’s never been more important for users to have access to high quality data.

Microsoft SQL Server is kind of a swiss army knife for most SME needs and workloads. However there are a handful of things that SQL Server will be better at, and there’s a handful of things Snowflake will be better at. Table of Contents Snowflake is great if you have big data needs. It offers scalable computing and limitless size in a traditional SQL and Data Warehouse setting. If you have a relatively small dataset or low concurrency/load then you won’t see the benefits of Snowflake.

Data maturity models measure the extent to which organizations have developed their data capabilities. They focus on a couple of dimensions that can include strategy, leadership, culture, people, governance, architecture, processes, and technology. Table of Contents The maturity levels of each of these dimensions may be measured along a continuum of four to six levels.

Analyzing gameplay metrics and log data is an essential part of the gaming industry, as it provides developers and publishers with valuable insights into how players interact with their games. Throughout this article, we will outline how analytics, observability, and reporting can aid you in improving your performance whether you are a games developer or a gaming enthusiast.

Without a doubt, Python stands out as one of the most sought-after and adaptable programming languages across the globe. In fact, some of the largest tech companies on the planet use Python, including Google, Facebook and Amazon. Python has been the go-to programming language for many developers, data scientists and researchers due to its ease of use, readability and robustness. But what exactly can Python do?

To get the most out of data centralization and democratization, treat data assets like products.

The digital revolution has sparked a wave of innovation as companies strive to meet consumers where they spend the most time — on web and mobile devices. To keep up with the demands that digital innovations place upon product markets, businesses are increasingly incorporating analytics into their products.

Can containerized deployments help your business? Are your customers’ data applications held back by basic, outdated dashboards and reports? Well, they’re not alone. As the digitization wave crashes over a post-pandemic market, many organizations are taking stock of their data tools and finding them lacking in comparison to other more modern solutions available. Gone are the days when simple self-service analytics would suffice for their users.

Do you ever notice how children have become more glued to gadgets than any generation before? Being highly exposed to digital experiences, including educational and entertainment applications comes with the need for more privacy protection for children's personal information.

Change data capture (CDC) allows you to move your data in real time. Learn what matters when choosing a solution for your organization.

Hitachi Vantara has once again been recognized as a leader and fast mover in the 2023 GigaOm Radar for Unstructured Data Management: Infrastructure-Focused Solutions, marking the third consecutive year we have achieved this honor. The report emphasizes the growing complexity of unstructured data management and highlights the importance of having a solution that can seamlessly handle data movement at scale automatically.

In the first three articles in this four-post series, my colleague Jason English and I explored DataOps observability, the connection between DevOps and DataOps, and data-centric FinOps best practices. In this concluding article in the series, I’ll explore DataOps resiliency – not simply how to prevent data-related problems, but also how to recover from them quickly, ideally without impacting the business and its customers.

The GPT euphoria got doused with some reality recently as Samsung employees realized they were sending false information to customers and Italy outright banned ChatGPT. The hype and concerns further accelerated last week with the godfather of AI, Hinton, resigning from Google, President Biden summoning AI leaders to Washington, and several stocks nose-diving on the threats generative AI poses to their business models.

Developer productivity is a complex subject for which there is no magic bullet. However, economic pressure, increased market competition and shorter delivery circles force many organisations to improve their efficiency and to open up new models of operations. Measuring, maintaining and eventually improving engineering productivity in an increasingly hybrid workplace are important discussions many organisations are having right now.

Are you considering integrating your external CRM or ERP system with the Salesforce platform? Look no further than Salesforce Connect, an integration tool that promises to display and use external data as if it were natively stored within Salesforce. This tool has been hailed as providing "seamless integration of data across system boundaries." But does it live up to the hype, or is there a better alternative out there?

Learn how Fivetran's cloud-native approach is revolutionizing the Composable CDP landscape and empowering organizations to build personalized experiences that drive business outcomes.

In partnership with Tumult Labs, BigQuery differential privacy can help to anonymize data, and will integrate with BigQuery data clean rooms.

Modern data stores give you the flexibility to tailor a data architecture to your needs.

The most secure data movement solution, now available on Google Cloud.

Out of the box Cloudera Data platform (CDP) performs superbly but over time, if data architecture, data engineering, and DevOps best practices are not maintained, you can get stuck maintaining the wild, wild west. In this six-part series, we’re focused on improving the health of your environment.

Use remote functions to handle DLP, unstructured data analysis and security or compliance constraints inside your BigQuery dataset.

Traditional BI has always been wrought with login friction. It’s very much a “pull” motion. In order to get the answers you need, you have to stop what you are doing “over there” and access the data you need “over here.” To disrupt this old way of thinking we launched ThoughtSpot for Sheets back in October 2022. And of course, the first thing a lot of customers asked was – "this is great, do you have something for Excel?" 🤦

Business is won or lost based on the quality of the experience you deliver to customers, partners, vendors, and employees. These experiences are built entirely on data. Harnessing data to deliver value is the single most powerful way to engage today’s demanding consumers—not to mention capturing market share and accelerating strategic decision-making. But there's a problem.

Learn how we utilize the Fivetran Log Connector to maintain our own Fivetran demo account.

Data-driven research, a product of using interdisciplinary scientific methods for extracting knowledge, is taking over the globe. According to McKinsey & Company’s Marketing Insights, data-driven organizations are 23 times more likely to acquire customers and 6 times as likely to retain them! Also termed “the fourth paradigm of science”, data-driven research has created opportunities for big data analysis in science and other industries.

Data-driven decision-making is your secret weapon, and a powerful product analytics tool is the key to unlocking its full potential. But in a sea of options, finding the perfect tool can feel like embarking on an epic quest. Fear not; we are here to guide you on an exhilarating journey to discover the ultimate product analytics tool that will catapult your company to unparalleled success.

Agencies can centralize data from 300+ sources and efficiently scale marketing analytics for clients through automation and valuable insights.

MariaDB and MySQL are two widely popular relational databases that boast many of the largest enterprises as their clientele. Both MariaDB and MySQL are available in two versions – A community-driven version and an enterprise version. But the distribution of features and development processes in the community and enterprise versions of MySQL and MariaDB differ from each other.

ThoughtSpot is helping customers reimagine how they use data, analytics, and AI with Google Cloud tools like BigQuery and Looker.

Learn about "immortal time" and how it impacts conversion rates.

View and modify your usage with ease — so you only use what you need.

Businesses everywhere have engaged in modernization projects with the goal of making their data and application infrastructure more nimble and dynamic. By breaking down monolithic apps into microservices architectures, for example, or making modularized data products, organizations do their best to enable more rapid iterative cycles of design, build, test, and deployment of innovative solutions.

Learn how growth consultant Kat Gardiner guides early-stage organizations in developing a scalable data strategy, without breaking the bank.

Every application provider has the same goals: to help their users work more efficiently, and to drive user adoption. But many companies fail to achieve this goal because they struggle to provide the reporting and analytics users have come to expect. The Definitive Guide to Embedded Analytics is designed to answer any and all questions you have about the topic. It will show you what embedded analytics are and how they can help your company.