Keep track of new releases for our Lite connectors with this regularly updated list.

When choosing to build or buy, consider whether the following challenges are worth the squeeze.

Data quality is fairly simple nomenclature to describe the state of the data being processed, analyzed, fed into AI, and more. But this modest little term belies an incredibly critical and complicated reality: that enterprises require the highest level of data quality possible in order to do everything from developing product and business strategies, and engaging with customers, to predicting the weather and finding the fastest delivery routes.

GitHub Actions is a powerful continuous integration and continuous delivery (CI/CD) platform that allows developers to automate build, test, and deployment pipelines. Workflows automatically build and test code whenever an event occurs, such as a pull request or a deployment of merged pull requests to production. Best of all, you can use it without leaving the comfort of your own repository!

Have you ever considered how much data a single person generates in a day? Every web document, scanned document, email, social media post, and media download? One estimate states that “on average, people will produce 463 exabytes of data per day by 2025.”

The lingering effects of the global pandemic are merging with inflation to create a perfect storm for retailers looking to find the right inventory stature for the seasons ahead. Companies are getting squeezed between rising supply chain costs and falling consumer confidence. To succeed in this volatile market, McKinsey suggests that retailers “accelerate decision-making tenfold.”

Data centers consume a lot of energy; some say it can be as much as 1.8% of total U.S. electricity consumption. It’s why power consumption, cooling costs, and space requirements are at the heart of the sustainable data center.

A good data governance strategy should benefit all users of your organization’s data—not just those with technical responsibility for it. Recent years have seen the increasing importance of data as a strategic asset, as several companies have used it to unlock and create value. Increasingly, companies are turning to data governance programs as a foundational pillar of their data strategy (like data mesh) to improve their data sets’ quality, consistency, usability, and security.

We often find it hard to remember the world we left behind, but cast your mind back, say, 20 years, and we lived in a very different world. Personal Computers and the internet were on the rise, and businesses were all becoming connected. This provided companies with immense opportunities in terms of collaboration and digital adoption, and on the flip side, it eased the distribution of computer viruses. Today we barely even think about our antivirus software and policies.

In this guide, you will learn about various methods to transfer your data from Salesforce to Redshift.

This Saturday, January 28th sees Data Privacy Day come round again, an international effort to empower individuals and encourage businesses to respect privacy, safeguard personal data and enable trust. As always this should act as a reminder that every individual within an organization requires a basic understanding of their internal privacy rules and regulations.

As the utilization of machine learning and MLOps (machine learning operations) continues to gain traction within organizations, it is imperative to stay abreast of the latest advancements and developments in the field.

Accessing data from the manufacturing shop floor is one of the key topics of interest with the majority of cloud platform vendors due to the pace of Industry 4.0 adoption. Industry 4.0, also known as the Fourth Industrial Revolution, refers to the emerging trend of technological transformation in manufacturing and related industries.

It’s 2023 and with the new year comes an opportunity to drive innovation, growth, and digital transformation with data in the face of ongoing economic turbulence. If Snowflake’s report, How to Win in Today’s Data Economy is any indication, data-driven organizations are poised to emerge as the winners of the year with 77% Data Economy Leaders, which is only 6% of those surveyed, experiencing annual revenue growth versus 36% of Data Economy Laggards, the lowest-performing survey group.

According to the U.S. Small Business Administration’s Office of Advocacy, small businesses account for approximately 99.9% of all businesses. That’s a massive chunk of the U.S. economy. While they are high in number, the issues they face are relatively higher, too. Nearly 35% of small business owners report ‌that they aren’t generating any profits, with inflation being the biggest of their worries.

We’ve established that we’re living in the defining decade of data. Data underpins the seismic technology shifts of the past few years, transforming the way we buy, work, make business decisions, even value our companies. As ThoughtSpot’s co-founder Ajeet Singh said, “Once in a generation, the opportunities to create a legacy increase massively. It happens when truly tectonic shifts happen in the ecosystem. We’re living through one of those times.”

A new Fivetran Airflow provider developed by Astronomer allows data engineers to run Fivetran data syncs more efficiently in Airflow 2.2+

With inflation and other disruptive market dynamics massively impacting consumer behavior, is it any surprise that personalization tops the list of strategic actions for CMOs in 2023? Yep, people tend to stick around when digital products and experiences fulfill their personal needs quickly and accurately. And topping the list of powerful tools for personalization? Machine learning and AI, of course, from product recommendations to targeted offers based on digital customer and behavioral data.

Data, data, data. It does seem we are not only surrounded by talk about data, but by the actual data itself. We are collecting data from every nook and cranny of the universe (literally!). IoT devices in every industry; geolocation information on our phones, watches, cars, and every other mobile device; every website or app we access—all are collecting data. In order to derive value from this avalanche of data, we have to get more agile when it comes to preparing the data for consumption.

Scania is at the forefront of a more autonomous, connected, electric future for the transportation industry. Find out why its Head of Data and Information Management uses data mesh—and Snowflake—to make it a reality. Scania is a global truck, bus, and industrial engine manufacturer and offers an extensive range of related services so its customers can focus on their core business.

Analytics engineer is the latest role that combines the technical skills of a data engineer with the business knowledge of a data analyst. They are typically coding in SQL, building dbt data models, and automating data pipelines. You could say they own the steps between data ingestion and orchestration. Whether you are a seasoned analytics engineer or new to the field, it’s important to continually learn new things and improve the work you’ve already done.

This Eckerson Group report gives you a good understanding of how the Unravel platform addresses multiple categories of data observability—application/pipeline performance, cluster/platform performance, data quality, and, most significant, FinOps cost governance—with automation and AI-driven recommendations.

“Data pipeline” and “Extract, Transform, Load” (ETL) are common phrases encountered in just about every data integration. But what’s the difference?

Marketing data integration is the process of combining marketing data from different sources to create a unified and consistent view. If you’re running marketing campaigns on multiple platforms—Facebook, Instagram, TikTok, email—you need marketing data integration. Why? Because being able to assimilate data from different channels and across multiple marketing touchpoints gives you visibility into the overall impact of a campaign, event, or another marketing effort.

Built with BigQuery: How Tamr delivers Master Data Management at scale and what this means for a data product strategy

Multivariate time series forecasting allows BigQuery users to use external covariate along with target metric for forecasting.

Google Cloud customers who want app-level encryption in hybrid cloud data warehouses can encrypt and decrypt that data outside BigQuery. Here’s how to do that securely.

The Chief Data Officer is arguably one of the most important roles at a company, particularly those that aspire to be data-driven. CDO appointments and the elevation of data leaders have accelerated in recent years, and the role has morphed as perceptions of data have evolved. Responsibilities span strategy and execution, people and processes, and the technology needed to deliver on the promise of data.

Recently, I published a blog on whether self-service BI is attainable, and spoiler alert: it certainly is. Of course, anything of value usually does require a bit of planning, collaboration, and effort. After the article was published, I began having conversations with technical leaders, analysts, and analytics engineers, and the topic of data modeling for self-service analytics came up repeatedly.

Extract, transform, load (ETL) is a critical component of data warehousing, as it enables efficient data transfer between systems. In the current scenario, Python is considered the most popular language for ETL. There are numerous Python-based ETL tools available in the market, which can be used to define data warehouse workflows. However, choosing the right ETL tool or your needs can be a daunting task.

Built with BigQuery: How to Accelerate Data-Centric AI development with Google Cloud and Snorkel AI.

The point of evidence is to guide decisions, so transforming a business into being evidence-based has to start with leaders.

Deploying models is becoming easier every day, especially thanks to excellent tutorials like Transformers-Deploy. It talks about how to convert and optimize a Hugging face model and deploy it on the Nvidia Triton inference server. Nvidia Triton is an exceptionally fast and solid tool and should be very high on the list when searching for ways to deploy a model. If you haven’t read the blogpost yet, do it now first, I will be referencing it quite a bit in this blogpost.

Organizations have been focused on enhancing customer experiences to enable quicker responses to services and to provide localized behavior for many years now. However, with the Internet of Things (IoT), Smart Cities, Gaming technologies and Self-Driving Cars going more mainstream, there is an even greater need for organizations to react faster to customer behavior and bring solutions closer to the customers.

Business Intelligence transforms raw data into actionable insights that support business decisions through reports, dashboards, and charts. You can use the blazer gem in Ruby on Rails to gather and display business metrics!

Good data hygiene means data is correct and easily used to draw insight. This definition then begs the question: How do you achieve it?

Retrieving data from a source, ensuring it suits business requirements, and moving that data into a target data source is critical to any data strategy. Low-code tools can help create robust and flexible ETL processes that automate your data loading.

Building a data-driven pricing platform for speed, scale and automation with BigQuery, Looker and more.

Backcountry, the specialty retailer of premium outdoor gear and apparel, shares key lessons on using a modern data stack to overcome data silos, complexities with legacy systems and improve its customer experience.

Data plays a profound role in finance. In fact, some might argue that finance professionals are some of the most data-driven individuals in an organization. That’s because finance data, and the insights you draw from it, can literally make or break a company. This is especially true in times of economic uncertainty, when businesses are trying to make data-driven decisions about where to invest and cut resource allocation.

BigQuery multi-statement transactions are now generally available and offer greater scale and additional functionality to handle the most complex of transactions.

Leading fintech companies AJ Bell, OrderPay and Tide are blazing a path toward better insights with a new, modern approach to data: self-service analytics.

As we move into 2023, I am very excited to see all of the predictions for data and analytics and what they mean to Kensu. I looked at different publications and spoke with various industry experts and analysts to see if there were any conclusions we could draw.

In just a couple of weeks, I will be in Singapore for our first in-person Sales Kick Off since 2020, and I can’t wait to join colleagues as we prepare our organization for the year ahead – first in the APAC region, quickly followed by Europe and the Americas.

Is your data warehouse modern enough? Learn the differences, benefits and available tools and strategies for easy migration.

Data warehousing is the process of collating data from multiple sources in an organization and store it in one place for further analysis, reporting and business decision making. Typically, organizations will have a transactional database that contains information on all day to day activities. Organizations will also have other data sources – third party or internal operations related. Data from all these sources are collated and stored in a data warehouse through an ELT or ETL process.

Five things to know about this topic: Just about every process used within a business generates some form of data. While some may see this information as useless, data analysis tools can turn it into a resource that helps your brand make better decisions in every aspect of its operations. Not all analytical tools are equal. However, the ones on this list can help you generate incredible insights that result in better decision-making.

Data teams and their business-side colleagues now expect—and need—more from their observability solutions than ever before. Modern data stacks create new challenges for performance, reliability, data quality, and, increasingly, cost. And the challenges faced by operations engineers are going to be different from those for data analysts, which are different from those people on the business side care about. That’s where DataOps observability comes in.

From cost-effectiveness to what adds business value — what Autodesk considers critical when deciding whether to buy versus build data pipelines.

Business analytics is the practice of using data and statistical analysis to help businesses make better decisions. This can involve analyzing data to identify trends, patterns, and relationships, and using that information to help businesses make better decisions about their operations, marketing, and strategy.

The move to the cloud continues at a fast pace and if your organization embraces the future of operational reporting, then you need a plan to ensure consistent enterprise-wide reporting during your cloud journey. A top challenge of cloud migration is the need to produce consolidated reporting and analytics that cover all your Oracle ERP instances.

As organizations look to scale up and improve the business value of their growing data volumes, certain data trends have garnered the attention of data and business professionals alike. With this growth promising to continue in the upcoming year, data leaders are looking to implement tools to enrich their organization’s data like never before. Here are seven trends you can watch for in the new year.

Snowflake customers leverage the Data Cloud to bring all their data together and capitalize on the near-infinite resources of the cloud. But how can this data be used to look ahead? How can we use yesterday’s evidence to plan for tomorrow? The answer—time series forecasting. Time series forecasting is one of the most applied data science techniques in business. It is used extensively in supply chain management, inventory planning, and finance.

Change data capture (CDC) identifies and captures data changes in source systems. Here’s a list of the best CDC tools.