In this article, we will discuss what exactly Elasticsearch is, alongside the considerations and common questions asked about this essential search engine.

Cybersecurity research website CyberNews recently interviewed Countly’s CEO, Onur Alp Soner, discussing everything from Countly’s origins to the role of cybersecurity in product analytics and how this might shape digital products in the near future.

We are excited to announce the general availability of Apache Iceberg in Cloudera Data Platform (CDP). Iceberg is a 100% open table format, developed through the Apache Software Foundation, and helps users avoid vendor lock-in. Today’s general availability announcement covers Iceberg running within key data services in the Cloudera Data Platform (CDP)—including Cloudera Data Warehousing (CDW), Cloudera Data Engineering (CDE), and Cloudera Machine Learning (CML).

Exabeam, a leader in SIEM and XDR, provides security operations teams with end-to-end Threat Detection, Investigation, and Response (TDIR) by leveraging a combination of user and entity behavioral analytics (UEBA) and security orchestration, automation, and response (SOAR) to allow organizations to quickly resolve cybersecurity threats.

One of the COVID-related changes that seems unlikely to be reversed is the increase in remote work. Particularly in tech, many companies have done away with office requirements entirely. For startups, this will have a profound effect on the ways they will grow, hire, and build culture. For individuals, it will mean figuring out a new routine and approach to daily life.

Going into Snowflake Summit 2022, I was excited to spend time with our customers and partners, and excited to be able to share some of the innovations we’ve been working on. And I was not disappointed! It felt great to experience the energy that only an in-person event can deliver. I relished talking to customers about how our products can help them meet and even surpass their business goals.

In a previous blog of this series, Turning Streams Into Data Products, we talked about the increased need for reducing the latency between data generation/ingestion and producing analytical results and insights from this data. We discussed how Cloudera Stream Processing (CSP) with Apache Kafka and Apache Flink could be used to process this data in real time and at scale. In this blog we will show a real example of how that is done, looking at how we can use CSP to perform real-time fraud detection.

Earlier in the quarter we had announced that BigQuery BI Engine support for all BI and custom applications was generally available. Today we are excited to announce the preview launch of Preferred Tables support in BigQuery BI Engine! BI Engine is an in-memory analysis service that helps customers get low latency performance for their queries across all BI tools that connect to BigQuery.

On the heels of announcing our $14.5M Series A and General Availability, we’re excited to be at the Data + AI Summit to unveil support for Continual on the Databricks Lakehouse. Increasingly, data and ML tool providers are embracing a data-centric approach to the ML workflow. The goal is to focus on what increasing drives ML – the data – compared to infrastructure, algorithms, or pipelines. At Continual we bet on data-centric AI from day one.

The modern data stack continues to attract companies who are looking for a quick onramp into the world of cloud-based analytics and/or actively modernizing their legacy data stacks. We've enumerated the benefits of the modern data stack in previous articles.

The digitalization of tax and operational transfer pricing processes can have a huge impact on a multinational company’s ability to efficiently forecast and report its tax liability.

Pipeline management is vital to the go-to-market strategy of any B2B business. A healthy pipeline is the closest you can get to a guarantee that you’ll make your targets by the end of the sales cycle. As the SVP of Business Operations at ThoughtSpot, I’m responsible for putting actionable insights into the hands of our sales and marketing functions to drive predictable pipeline growth.

Learn how Fivetran Transformations for dbt Core can help your data analyst teams find efficiencies and optimize data pipelines.

BigQuery BI Engine is a fast, in-memory analysis service that lets users analyze data stored in BigQuery with rapid response times and with high concurrency to accelerate certain BigQuery SQL queries. BI Engine caches data instead of query results, allowing different queries over the same data to be accelerated as you look at different aspects of the data.

Fast and clean. These two words define the ideal financial close process. This standard is held up as a measure of a finance or accounting department’s effectiveness. Companies are expected to get the financial close process done within a standard business week. This demonstrates competence, resource efficiency, and good management. An efficient financial consolidation and close process does two vital things.

Much of the hype around big data and analytics focuses on business value and bottom-line impacts. Those are enormously important in the private and public sectors alike. But for government agencies, there is a greater mission: improving people’s lives. Data makes the most ambitious and even idealistic goals—like making the world a better place—possible.

Since we launched Talend Data Fabric in 2015, we’ve believed strongly that merely focusing on the mechanics of data — capturing, moving, and storing data — is not enough to become data-driven. Everyone in the organization must be able to easily find, trust, and use data. That’s what data health is all about, and that’s what Talend makes possible. Forrester looked at the 15 software providers that matter the most when it comes to Enterprise Data Fabric.

Across the globe, cloud concentration risk is coming under greater scrutiny. The UK HM Treasury department recently issued a policy paper “Critical Third Parties to the Finance Sector.” The paper is a proposal to enable oversight of third parties providing critical services to the UK financial system.

Learn key considerations in establishing data residency requirements.

In order to better serve their customers and users, digital applications and platforms continue to store and use sensitive data such as Personally Identifiable Information (PII), genetic and biometric information, and credit card information. Many organizations that provide data for analytics use cases face evolving regulatory and privacy mandates, ongoing risks from data breaches and data leakage, and a growing need to control data access.

Indonesia’s largest hyperlocal company, Gojek has evolved from a motorcycle ride-hailing service into an on-demand mobile platform, providing a range of services that include transportation, logistics, food delivery, and payments. A total of 2 million driver-partners collectively cover an average distance of 16.5 million kilometers each day, making Gojek Indonesia’s de-facto transportation partner.

In your machine learning projects, have you ever wondered “why is model Y is performing better than Z, which dataset was model Y trained on, what are the training parameters I used for model Y, and what are the model performance metrics I used to select model Y?” Does this sound familiar to you? Have you wondered if there is a simple way to answer the questions above? Data science experiments can get complex, which is why you need a system to simplify tracking.

It’s that time of year again. Conference season is upon us! And, for the first time in what feels like a lifetime, the data ecosystem is getting back together in person. It couldn’t come at a more important time. The decade of data is upon us, as we unveiled at our own customer conference Beyond 2022. The opportunity is greater than ever before. So, too, is the need to change.

In the second blog of the Universal Data Distribution blog series, we explored how Cloudera DataFlow for the Public Cloud (CDF-PC) can help you implement use cases like data lakehouse and data warehouse ingest, cybersecurity, and log optimization, as well as IoT and streaming data collection. A key requirement for these use cases is the ability to not only actively pull data from source systems but to receive data that is being pushed from various sources to the central distribution service.

COVID-19 introduced an unprecedented level of volatility in world markets, and the shockwaves that arrived in its wake exposed a wide chasm between two main types of multinational organizations: Those with agile internal processes and those without. In a world built on complex and globalized supply chains, COVID-19 tested that internal agility, sometimes to breaking point.

Never before has data become so prevalent in everything we do. Sorting out the best way to make sense of incoming terabytes of data has turned into an extreme sport. Likewise, it has become a life-or-death decision in every organization, regardless of their level of maturity, to determine an analytics strategy to harness the potential power of all that data without running the risk of overwhelming teams and paralyzing processes.

Cloudera customers run some of the biggest data lakes on earth. These lakes power mission critical large scale data analytics, business intelligence (BI), and machine learning use cases, including enterprise data warehouses. In recent years, the term “data lakehouse” was coined to describe this architectural pattern of tabular analytics over data in the data lake.

Every large enterprise organization is attempting to accelerate their digital transformation strategies to engage with their customers in a more personalized, relevant, and dynamic way. The ability to perform analytics on data as it is created and collected (a.k.a. real-time data streams) and generate immediate insights for faster decision making provides a competitive edge for organizations.

Our new feature helps you implement Type 2 slowly changing dimensions for your historical database analytics with no coding needed.

Data integration plays a foundational role in the progression to data and analytics mastery.

Qlik enhances analytics exploration in Snowflake by launching Direct Query, a new capability that allows Qlik Sense applications and dashboards to query Snowflake directly using SQL pushdown.

In this tutorial, we’re going to build an interactive customer Churn Insights Dashboard using the open-source Python framework, Streamlit, and the Continual predictions generated in Part 1: Snowflake and Continual Quickstart Guide. In Part 1, we connected Continual to Snowflake and used a simple dataset of customer information, activity, and churn status to build and operationalize a machine learning model in Continual to predict the likelihood of a customer churning.

Google recently announced that the current Google Analytics 3 (Universal Analytics) will come to an end in July 2023 (now extended until October 2023) and they’ve encouraged all current users to start using the new GA4. Google Analytics 4 is the new version of the current GA reporting portal that current users have used to analyse the performance of their sites.

Sometimes the need for processing power you or your team requires is very high one day and very low another. Especially in machine learning environments, this is a common problem. One day a team might be training their models and the need for compute will be sky high, but other days they’ll be doing research and figuring out how to solve a specific problem, with only the need for a web browser and some coffee.

Snowflake has once again transformed data management and data analytics with our newest workload—Unistore. For decades, transactional and analytical data have remained separate, significantly limiting how fast organizations could evolve their businesses. With Unistore, organizations can use a single, unified data set to develop and deploy applications, and analyze both transactional and analytical data together in near-real time.

As the Senior Director for Marketing Ops at ThoughtSpot, I’m the owner of our full marketing and sales tech stack. So, you know. No pressure. I started out as a Marketing Analyst — certified Tableau superuser, the whole deal. And I’ve got to say what I can do now versus what I could do then is night and day. What used to take me hours in Tableau back in 2015 literally takes me minutes in ThoughtSpot.

Geospatial data has many uses outside of traditional mapping, such as site selection and land intelligence. Accordingly, many businesses are finding ways to incorporate geospatial data into their data warehouses and analytics. Google Earth Engine and BigQuery are both tools on Google Cloud Platform that allow you to interpret, analyze, and visualize geospatial data.

We are excited to announce that Cloudera is named as a 2022 Gartner Peer Insights Customers’ Choice for Cloud Database Management Systems (DBMS). Peer Insights is a user review site, the technology professional’s “go-to” destination for information on customer experience. Gartner Peer Insights collects anonymous customer reviews on select product categories. To date, Gartner has collected over 450,000 reviews for 18,000 products in over 425 categories.

Cloud migration is a daunting prospect, especially considering the expense of installation, training and embedded new processes. But the benefits of enhanced functionality, the power of the cloud, and increased ROI are reason enough for organizations across the world to convert every day. Cloud enterprise resource planning (ERP) software is ideal for a variety of applications, including managing multiple departments and CRM integration.

We’re proud to share that Iguazio has been named in Gartner's 2022 Market Guide for Data Science & Machine Learning Engineering Platforms. According to Gartner, “The AI & data science platform market is due to grow to over $10 billion by 2025 at a 21.6% compounded annual growth rate.

Here at Cloudera, we’re committed to helping make the lives of data practitioners as painless as possible. For data scientists, we continue to provide new Applied Machine Learning Prototypes (AMPs), which are open source and available on GitHub. These pre-built reference examples are complete end-to-end data science projects. In Cloudera Machine Learning (CML), you can deploy them with the single click of a button, bringing data scientists that much closer to providing value.

SAP’s library of pre-defined reports for Finance and Controlling (FICO) is great for addressing some of the core tasks associated with finance and accounting. Those reports align well with accounting standards under GAAP and IFRS. Unfortunately, they rarely do a good job of addressing the kind of reporting needed to make informed managerial decisions.

As a very hands-on VP of Product, I have many, many conversations with enterprise data science teams who are in the process of developing their MLOps practice. Almost every customer I meet is in some stage of developing an ML-based application. Some are just at the beginning of their journey while others are already heavily invested. It’s fascinating to see how data science, a once commonly used buzz word, is becoming a real and practical strategy for almost any company.

In the first blog of the Universal Data Distribution blog series, we discussed the emerging need within enterprise organizations to take control of their data flows. From origin through all points of consumption both on-prem and in the cloud, all data flows need to be controlled in a simple, secure, universal, scalable, and cost-effective way.

When it comes to hybrid cloud and digital transformation, it’s all about application services and leveraging appropriate on-premise, service provider, and hyperscaler cloud resources and services seamlessly and efficiently.

Google’s data cloud enables customers to drive limitless innovation and unlock the value of their data via its robust offerings under a single, unified interface. By migrating their data ecosystems to Google Cloud, organizations are able to break down their data silos and harness the full potential of their data. However, historically, migrating data warehouses has not been an easy task.

Odds are good that your team already knows that Wands for Oracle puts finance teams in control of their own reporting. Offering purpose-built software that deeply integrates with Oracle E-Business Suite in Excel, Wands gives you quick access to the real-time data you need, when you need it, without relying on IT or resorting to manual data dumps.

People analytics can help you understand employee pain points and take steps to retain top talent.

The algorithm team at WSC Sports faced a challenge. How could our computer vision model, that is working in a dynamic environment, maintain high quality results? Especially as in our case, new data may appear daily and be visually different from the already trained data. Bit of a head-scratcher right? Well, we’ve developed a system that is doing just that and showing exceptional results!

‍ Today, we’re excited to announce the general availability of Continual, the missing AI layer for the modern data stack. We’ve also raised a $14.5M Series A, led by Innovation Endeavors and joined by Amplify Partners, Illuminate Ventures, Inspired Capital, Data Community Fund, Activation, New Normal, GTMfund, and angels Tomer Shiran, the founder of Dremio, and Tristan Handy, the founder of dbt Labs.

Fivetran and our modern data stack partners are poised to thrive.

Making insights for your business isn’t easy. You're expected to always do more, do it faster, all without costing a small fortune. But how can you expect to do this when you’re using the wrong kind of analytics in the first place? Let’s explore. You may have heard of the 4 different types of analytics (the image below from Gartner helps visualize each type and how we use them) Think about how many post-mortem meetings you’ve had. Hindsight is important, of course!

We live in a hybrid data world. In the past decade, the amount of structured data created, captured, copied, and consumed globally has grown from less than 1 ZB in 2011 to nearly 14 ZB in 2020. Impressive, but dwarfed by the amount of unstructured data, cloud data, and machine data – another 50 ZB.

The pursuit of business insights is a central focus the enterprise. More than a mere natural resource, data is fueling strategic decision making and as a result, the future of business. Hitachi Vantara is committed to helping accelerate data-driven experiences with cloud-ready infrastructure, advanced data management solutions, and expert services. But no company or provider in the 21st century can go it alone.

Cybersecurity is a data problem at its core. Yet, security teams haven’t achieved tremendous success in utilizing the modern data stack that data analytics teams have enjoyed for years. Security teams face constant pressure from vulnerabilities and breaches in their infrastructure and supply chains because they remain on a proverbial island with antiquated technology. Cybersecurity leaders must uplevel their strategies by implementing a modern security data lake.

Ever wondered what our most-used components are? Here at Talend, one of my “Shadow IT” jobs is to report on component usage. If you've ever used Talend Studio (either the open source Talend Open Studio or the commercial version) you most likely already know and love the component tMap.

Data scientists and machine learning engineers in enterprise organizations need to fully understand their data in order to properly analyze it, build models, and power machine learning use cases across their business. Due to the lack of tooling specifically designed for data discovery, exploration, and preliminary analysis, this presents a significant challenge for these teams.

Today, we are celebrating two product enhancements suggested by Max Hulten of Planspace that were delivered in the latest version of Bizview. We appreciate Max taking the time to share his ideas with us. Recently, we had the pleasure of speaking with him to learn more about his ideas and why he sees value in providing feedback to insightsoftware. Max Hulten, Planspace Co-Founder and Partner has been working with Bizview since 2015, implementing the product for countless customers.

Since 2015, the Cloudera DataFlow team has been helping the largest enterprise organizations in the world adopt Apache NiFi as their enterprise standard data movement tool. Over the last few years, we have had a front-row seat in our customers’ hybrid cloud journey as they expand their data estate across the edge, on-premise, and multiple cloud providers.

A little over a year ago, I found myself feeling stuck in my role as a data engineer. I had majored in business in college and was looking to connect more with that side of things. I enjoyed my tasks as a data engineer but I wanted more flexibility and creativity. I wanted to be involved in business decisions rather than my tasks already being decided for me.

With Fivetran’s new App Reporting data model, you can easily roll your Apple App Store and Google Play data into a unified schema for seamless reporting.

Product Managers, especially those in start-ups, have no easy job to do: product tweaks, project management, QA, release notes, you name it. With such a dynamic and all-encompassing role, things can get tricky. So much so that, in a recent poll we conducted via LinkedIn, we found out that “Testing and Launch” is the second most problematic stage in the life of a digital product, according to almost 27% of product managers.

Data visualization is the art of representing complex data sets in a visually appealing way. It can help your reader better understand what they're looking at, and it's an ideal way to make sense of large or confusing sets of data. For example, imagine reading a report about a study that involved tracking participants' sleep patterns for three months.

How we eat, exercise, work, and rest play an important role in influencing our health outcomes. It’s been established that healthcare and life sciences (HCLS) organizations can improve health outcomes when they have access to this type of data on patients to inform real-world evidence.