Resources and insights

Unlock the power of generative AI in data engineering with Prophecy’s Data Copilot.

Top 10 reasons to visit Prophecy at Data + AI Summit 2023

Empowering business data users to quickly and easily build scalable data pipelines on the lakehouse

Learn key steps to follow, how to define the ETL pipeline using Spark APIs and dataframes, and best practices for testing and optimizing pipelines for maximum efficiency

Bringing the power of low-code data engineering to the masses

Learn how the Texas Rangers use Prophecy and Databricks Lakehouse as the foundation of their data mesh architecture to gain a competitive advantage with low-code data engineering.

Leverage Prophecy’s low-code approach and integration with dbt Core to build SQL data pipelines without compromising on engineering best practices

Discover how Prophecy 3.0 arms all data users with low-code SQL, so they can quickly and easily build scalable data pipelines, resulting in highly impactful data products.

Learn essential skills and techniques to empower non-technical data practitioners to build ETL pipelines without relying on data engineering.

A rising number of enterprises are adopting the lakehouse to unite their analytics projects and foster innovation on a shared, cloud-based platform.

Join this webinar to learn how the Texas Rangers are creating a competitive advantage in professional baseball by using data analytics and AI to improve player performance and evaluate new talent.

Let's create a pipeline using Prophecy's visual, low-code interface for Spark.

Let's get started with PySpark using a visual interface to generate open-source Spark code.

In Part 1, we learned an easy way to troubleshoot a data pipeline using historical, read-only metadata. Now, I want to dig in and polish my individual spark data frames.

Let’s take advantage of Spark’s interim metadata to understand our Spark job behavior with low-code tooling.

For enterprises, navigating the cloud transition can be tricky. This describes the two paradigms for ETL in the cloud, and the factors to consider when choosing one.

Prophecy also provides a highly automated replacement for legacy ETL products to accelerate the journey to open source and cloud computing.

Just a few days ago, we announced a Prophecy for Databricks. In this blog post — Part 2 of that announcement — we dig into how Prophecy makes data engineering simple for any data practitioner on Databricks.‍

In this blog, we learn about the concept of financial reporting, its importance, the various challenges faced while generating an efficient and reliable financial report and how Prophecy makes the complete process easy sailing.

Learn more about the evolution of data architectures, from traditional ETL through two-tier and modern data lakehouses. Explore how Prophecy can enable you to leverage the lakehouse by making common use cases like advanced merges and slowly changing dimensions significantly easier.

Prophecy is delighted to announce we raised a $25M Series A led by Insight Partners, with participation from SignalFire, Dig Ventures and Berkeley SkyDeck.

We're excited to be a part of Databricks Partner connect! Read this blog to learn how Databricks Partner Connect makes getting started with Prophecy simple via single-click signup and sign-in.

In a Data Engineering Battle, Prophecy presented a complete, low-code data engineering product with a session for low-code Spark, low-code Airflow and column-level lineage that hundreds attended. Here are the poll results.

Prophecy presented a complete, low-code data engineering product at the Data + AI Summit 2021. Hundreds attended our sessions on low-code Spark, low-code Airflow and column-level lineage. Here are the results from the polls we conducted there.

Learn about the four main pillars of productive, powerful, low-code data engineering on Spark. See all the pieces in action!

Announcing the launch of Prophecy SaaS!

The disruption is agile software practices in data engineering made usable for the many. Allow me to explain.

We discuss the two cloud data engineering architectures and advise which one to pick as you move your ETL to the cloud.

We're excited to share what we have built with you. Sign up and let us know what you think!

Prophecy has innovated in metadata management by merging code on git with metadata, making it code-first.

Prophecy computes column-level lineage from code for Spark code on Git.

Prophecy IDE enables visual and code developers to produce high-quality Spark code.

Parser combinators allow you to develop sophisticated parsers quickly and see how to use them.

Customer discovery is essential to figuring out product market fit. Learn how to do it well and when to use it.

Introducing Prophecy.io — a high-performance, zero-compromise, cloud-native data engineering product powered by Spark for enterprise data engineering teams.