The Extract, Transform, and Load process (ETL for short) is a set of procedures in the data pipeline. It collects raw data from its sources (extracts), cleans and aggregates data (transforms) and saves the data to a database or data warehouse (loads), where it is ready to be analyzed. A well-engineered ETL process provides true business value and benefits such as: Novel business insights. The entire ETL process brings structure to your company’s information.

While the word “data” has been common since the 1940s, managing data’s growth, current use, and regulation is a relatively new frontier. Governments and enterprises are working hard today to figure out the structures and regulations needed around data collection and use. According to Gartner, by 2023 65% of the world’s population will have their personal data covered under modern privacy regulations.

Domino’s Pizza, one of the world’s top restaurant brands, already knows how to translate data into great customer experiences and stronger sales with Talend. Over the past five years, the company has used Talend to integrate 100’s data sources into a single source of customer information — and has harnessed that data to improve everything from personalized promotions to logistics to financial forecasting. The latest ingredient to their success?

“What should you and your business focus on when trying to create better customer journeys and beat competition?” That was the question we asked Countly data captains (also known as Countly customers) when trying to determine how well they collect customer experience metrics and how well they were using that data to make data-driven decisions.

Organizations today are expected to fully utilize their data to speed decisions and drive business value. Modern analytics can help.

Cloudera DataFlow for the Public Cloud (CDF-PC) is a cloud-native service for Apache NiFi within the Cloudera Data Platform (CDP). CDF-PC enables organizations to take control of their data flows and eliminate ingestion silos by allowing developers to connect to any data source anywhere with any structure, process it, and deliver to any destination using a low-code authoring experience.

Today, we’re excited to announce that DataFlow Functions (DFF), a feature within Cloudera DataFlow for the Public Cloud, is now generally available for AWS, Microsoft Azure, and Google Cloud Platform. DFF provides an efficient, cost optimized, scalable way to run NiFi flows in a completely serverless fashion. This is the first complete no-code, no-ops development experience for functions, allowing users to save time and resources.

Developers already love Fivetran’s docs. Our new API reference gives them even more to love.

Data teams have the impossible task of delivering everything (data and workloads) everywhere (on premise and in all clouds) all at once (with little to no latency). They are being bombarded with literature about seemingly independent new trends like data mesh and data fabric while dealing with the reality of having to work with hybrid architectures. Each of these trends claim to be complete models for their data architectures to solve the “everything everywhere all at once” problem.

Chief Data & Analytics Officer UK (CDAO UK) is the United Kingdom’s premier event for senior data and analytics executives. The three-day event, with more than 200 attendees and 50+ industry-leading speakers, was packed with case studies, thought leadership, and practical advice around data culture, data quality and governance, building a data workforce, data strategy, metadata management, AI/MLOps, self-service strategies, and more.

Are you efficiently unifying, modeling, analyzing, and activating all the data you need to drive impactful marketing campaigns and customer experiences? For years, marketing teams have struggled to operate from a single view of the customer and their business, essential to powering personalized experiences and measuring impact on key KPIs such as sales, growth, and profitability. Today, only half of all marketers have a unified view of the customer.

In 2022, digital natives and traditional enterprises find themselves with a better understanding of data warehousing, protection, and governance. But machine learning and the ethical application of artificial intelligence and machine learning (AI/ML) remain open questions, promising to drive better results if only their power can be safely harnessed.

Learn how to use Fivetran Transformations for dbt Core* to automatically generate views in your destination of fresh, reliable formula field values from Salesforce.

In a recent blog, Cloudera Chief Technology Officer Ram Venkatesh described the evolution of a data lakehouse, as well as the benefits of using an open data lakehouse, especially the open Cloudera Data Platform (CDP). If you missed it, you can read up about it here.

In a recent blog, Cloudera Chief Technology Officer Ram Venkatesh described the evolution of a data lakehouse, as well as the benefits of using an open data lakehouse, especially the open Cloudera Data Platform (CDP). If you missed it, you can read up about it here.

Insurance carriers are always looking to improve operational efficiency. We’ve previously highlighted opportunities to improve digital claims processing with data and AI. In this post, I’ll explore opportunities to enhance risk assessment and underwriting, especially in personal lines and small and medium-sized enterprises.

The catalyst of innovation and transformation is data. The companies that recognize the power of data and wield it to drive business transformation are seeing positive impacts on their business outcomes, as indicated in our report, How to Win in the Data Economy. We surveyed 1,000 senior business and technology executives to gauge the impact the data industry is having on their businesses, and to what extent companies are embracing the opportunity to become data leaders.

In 1895, a German physicist was trying to determine if he could observe cathode rays escaping from a glass tube and noticed an unexpected glow on a fluorescent screen several feet away. On further examination, it turned out to be a different kind of radiation that we now know as X-ray. Fast forward to today and you can’t even imagine diagnosing many medical problems without the X-ray.

Your database schema is the foundation for everything you do with data. Learn our essential best practices in our complete guide.

Every data team wants to make insights more actionable for frontline business users. The only question is how. You know they spend the majority of their time in business-critical tools like HubSpot, Slack, and Microsoft Teams. So why not bring the data-driven insights created in ThoughtSpot to the apps they use most? With ThoughtSpot Sync, you can. Starting today, ThoughtSpot customers will be able to send insights directly from ThoughtSpot to Google Sheets, Slack, and Microsoft Teams.

Fivetran Metadata API enables the tracking of data “in-flight” from source to destination as it moves through Fivetran-managed pipelines — delivering full visibility of data movement across systems.

As economic conditions change, retail brands’ reliance on ever-growing customer demand puts these companies at financial and even existential risk. Top-line revenue and active customer growth do not equal profitable growth.

Snowflake removes the need to perform maintenance tasks on your data platform and provides you with the freedom to choose your data model methodology for the cloud. When attempting to keep the cost of data processing low, both data volume and velocity can make things challenging.

The identity team at Cloudera has been working to add the System for Cross-domain Identity Management (SCIM) support to Cloudera Data Platform (CDP) and we’re happy to announce the general availability of SCIM on Azure Active Directory! In Part One we discussed: CDP SCIM Support for Active Directory, which discusses the core elements of CDP’s SCIM support for Azure AD.

In these busy times that we live in, technologies are expanding, evolving, and transforming every day. We are today introduced to new technical terms frequently, giving us a sense that digital space has taken an invincible part in our lives. Whenever we talk about technology, we may change the operating methods or overall interaction process, but one thing remains constant – the data. In other words, whatever we do with technology, we are constantly generating data. Table Of Contents.

Value Stream Management (VSM) is about empowering delivery organizations to measure, mitigate, and monitor complexity. Simply put, it aims at improving the flow of value in your organisation. The VSM Consortium recently released their highly anticipated report on “The State of Value Stream Management 2022” . In this post we recap some of the findings and look at it specifically from a software engineering and platform engineering point of view.

Today every company is a data company. And even with all the great new data systems and technologies, it’s people—data teams—who unlock the power of data to drive business value. But today’s data teams are getting bogged down. They’re struggling to keep pace with the increased volume, velocity, variety, complexity—and cost—of the modern data stack. That’s where Unravel DataOps observability comes in.

What if I want to serve a Huggingface model on ClearML? Where do I start? In general, machine learning engineers know by now that a good model serving engine is invaluable when serving models in production. These days, NVIDIA’s Triton inference engine is a popular option to do so, but it is lacking in some respects.

In its 2022 Magic Quadrant™ for Data Integration Tools report, Gartner® observes that “organizations are increasingly seeking a comprehensive range of improved data integration capabilities to modernize their data, analytics and application infrastructures.”

How long do users stay on your website? 50+ experts spill their secrets on how to improve your average session duration and what numbers to aim for.

Apache Ozone is a distributed, scalable, and high-performance object store, available with Cloudera Data Platform (CDP), that can scale to billions of objects of varying sizes. It was designed as a native object store to provide extreme scale, performance, and reliability to handle multiple analytics workloads using either S3 API or the traditional Hadoop API.

Visualizing data is an important aspect of presenting insights clearly. But it's not always easy to create an effective visualization that people will understand on their first glance, or even second. So how do you create the kinds of graphs and tables that leave key stakeholders thinking, " Wow! I need this information!" In this post, we will discuss the top nine best practices for data visualization.

In today’s competitive environment, organizations need to quickly and easily make decisions based on real-time data. That’s why we’re announcing Datastream for BigQuery, now available in preview, featuring seamless replication from operational database sources such as AlloyDB for PostgreSQL, PostgreSQL, MySQL, and Oracle, directly into BigQuery, Google Cloud’s serverless data warehouse.

As organizations invest ever more heavily in modernizing their data stacks, data teams—the people who actually deliver the value of data to the business—are finding it increasingly difficult to manage the performance, cost, and quality of these complex systems. Data teams today find themselves in much the same boat as software teams were 10+ years ago. Software teams have dug themselves out the hole with DevOps best practices and tools—chief among them full-stack observability.

What’s harder than finding the right data architecture? Finding the right dedicated partner. Adverity gets both with Snowflake. Learn how the two organizations are moving into new markets and supplying even more reliable marketing data to Adverity customers. When a fast-growing SaaS business looks to expand its client base, it normally encounters two major challenges: In many cases, an external data solution provider can only help solve the scalability challenge.

Some time ago, we covered the key metrics that a Product Manager in a fintech organization should make a top priority when determining their KPIs, breaking them down into five groups: Session-based data, Customer Feedback, Technical Metrics, Action Stats, and Revenue. With that in mind, we conducted a series of surveys on LinkedIn, asking PMs in the fintech industry which of those groups were the most important for them while running digital product analytics.

July brings summer vacations, holiday gatherings, and for the first time in two years, the return of the Massachusetts Institute of Technology (MIT) Chief Data Officer symposium as an in-person event. The gathering in 2022 marked the sixteenth year for top data and analytics professionals to come to the MIT campus to explore current and future trends. A key area of focus for the symposium this year was the design and deployment of modern data platforms.

Successful teams blend the art of sales with a data-driven pipeline strategy.

A key part of business is the drive for continual improvement, to always do better. “Better” can mean different things to different organizations. It could be about offering better products, better services, or the same product or service for a better price or any number of things. Fundamentally, to be “better” requires ongoing analysis of the current state and comparison to the previous or next one. It sounds straightforward: you just need data and the means to analyze it.

When I first started my role as an analytics engineer, I was tasked with rewriting a bunch of data models that were written in the past by contractors. These models were taking over 24 hours to run and often failed to run at all. They were poorly thought out and contained a bunch of “quick fix” code rather than being designed with the entire flow of the model in mind.

We’re back with another Job of the Week – but this time, we’re taking a step back to cover a concept we’ve skipped over in previous segments: OAuth2 authentication. Richard’s demonstrations often show simpler shortcuts to accessing data – but these shortcuts may not always be practical in real-world examples. Never fear! We’ll arm you with the know-how you need to make your data hacks just as impressive in real life.

Imagine having self-service access to all business data, anywhere it may be, and being able to explore it all at once. Imagine quickly answering burning business questions nearly instantly, without waiting for data to be found, shared, and ingested. Imagine independently discovering rich new business insights from both structured and unstructured data working together, without having to beg for data sets to be made available.

For the third year in a row, we were recognized for our "ability to execute" and "completeness of vision.".

The management of data assets in multiple clouds is introducing new data governance requirements, and it is both useful and instructive to have a view from the TM Forum to help navigate the changes.

It’s that time of year - back to school, back to books, and our annual must-read books for data and analytics leaders. Given the pace of change in our industry, continuous learning is a must, whether through networking, podcasting, or reading. To cull this year’s list, I focused mainly on books published in the last two years with the themes of data, analytics and AI. I scoured lists and reviews on Amazon, solicited ideas from social networks and got to reading.

How can you ensure data quality and security across your data analytics pipeline? With data governance – the exercising of authority and control over your data assets. It includes tracking, maintaining and protecting data at every stage of the lifecycle.

We are now well into 2022 and the megatrends that drove the last decade in data—The Apache Software Foundation as a primary innovation vehicle for big data, the arrival of cloud computing, and the debut of cheap distributed storage—have now converged and offer clear patterns for competitive advantage for vendors and value for customers.

Without automated data integration, broad trust in AI and the right expertise, organizations will struggle to mature their AI capabilities.

In part 1 of this blog series, we looked at how Snowflake supports the GEOGRAPHY geospatial data type, which works with the earth as an ellipsoid, measuring distances over a curvature and plotting objects using the latest World Geodetic System, WGS84.

In the wake of the disruption caused by the world’s turbulence over the past few years, the telecommunications industry has come out reasonably unscathed. There remain challenges in workforce management, particularly in call centers, and order backlogs for fiber broadband and other physical infrastructure are being worked through. But digital transformation programs are accelerating, services innovation around 5G is continuing apace, and results to the stock market have been robust.

Stale data costs businesses in poor outcomes and decision-making. With Fivetran and Snowflake, it’s simple to gain real-time access to your SAP ERP data and maximize its value.

Data backup is the last line of defense when a cyberattack occurs, especially when the attack is ransomware. With robust data backup technologies and procedures, an organization can return to a point-in-time prior to the attack and return to operations relatively quickly. But as data volumes continue to explode, ransomware attacks are growing more sophisticated and beginning to target that precious backup data and administrator functions.

Our six new models are aggregated on the most common granularities for ad-related metrics, so marketing analysts can hit the ground running.

Apache HBase has long been the database of choice for business-critical applications across industries. This is primarily because HBase provides unmatched scale, performance, and fault-tolerance that few other databases can come close to. Think petabytes of data spread across trillions of rows, ready for consumption in real-time.

Modern data pipelines have become more business-critical than ever. Every company today is a data company, looking to leverage data analytics as a competitive advantage. But the complexity of the modern data stack imposes some significant challenges that are hindering organizations from realizing their goals and realizing the value of data.

IT decision-makers offer insights into their modernization plans, which include automating data integration and adopting lakehouse technology.

Cloud complexity is an inevitability. Regardless of where an organization may be on their cloud journey – on-prem, in the public cloud, or managing an expanding hybrid cloud – the reality is managing the enterprise isn’t getting any easier. Demand continues to rise for greater access to more data across the organization to do things like run analytics and machine learning and to automate more processes.

You want to enable analytics, data science, or applications with data so you can answer questions, predict outcomes, discover relationships, or grow your business. But to do any of that, data must be stored in a manner to support these outcomes. This may be a simple decision when supporting a small, well-known use case, but it quickly becomes complicated as you scale the data volume, variety, workloads, and use cases.

Software company Slack is on a mission to make work simpler, more pleasant, and more productive. Millions of users across more than 150 countries use Slack to collaborate with team members, connect other tools and services, and access information. Marketers at Slack rely on large amounts of data to build custom audiences, manage subscriber consent preferences, and measure campaign performance.