Systems | Development | Analytics | API | Testing

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.

When researching your next ETL and ELT tool, you should consider Keboola as one of the best Fivetran alternatives. In this blog article, we’re going to compare Keboola and Fivetran side-by-side and show you how Keboola can simplify your data operations. We’re going to evaluate both tools based on these critical product features: Here is a quick breakdown summary of the comparison between Keboola and Fivetran: ‍

Happy New Year from the Yellowfin team, and welcome to our 2023 wrap-up! Following a year full of product feature updates, company changes and new initiatives, this blog provides a helpful summary for all our customers and followers on our future 2023 product roadmap for the Yellowfin embedded analytics suite, and a look back at last year’s biggest news.

In general, the concepts of data literacy and creating successful data pipelines seem totally disconnected. Data literacy involves insuring that data consumers have the knowledge and capabilities to understand and interact with data in a way that will provide them with the answers and value they need to do their jobs and benefit their organizations. While data pipelines require technical expertise to move, connect, and store data across the company's data ecosystem.

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and first magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes.

In the data landscape, the people are represented by two separate yet equally important groups. The data engineers who design the Lego blocks and the data scientists who build something extraordinary out of them. These are their stories. DUN DUN! And we’re back! Last time, we went over the toolkit needed to get your foot in the door as a data engineer. You’ve gotten over the first hurdle, but I hope you haven’t fallen prey to the Dunning-Kruger Effect.

Self-service analytics is fast becoming a necessity, not a luxury, in the modern enterprise. More businesses want to provide staff with self-service BI tools they can all use, without needing IT help or technical knowledge. This helps drive a data-driven culture across the organization, open up access to data to more people, and unlock actionable insights.

For the past 30 years, the primary data source for business intelligence (BI) and data visualization tools has generally been either a data warehouse or a data mart. But as enterprises today struggle to cope with the growing complexity, scale, and speed of data, it’s becoming clear that the data tools of 30 years ago weren’t designed to handle the enterprise data management challenges of today - especially with the growing variety and amounts of data that enterprises are generating.