Introducing Confluent Cloud OpenSearch Sink Connector

Amazon OpenSearch is a popular fully managed analytics engine that makes it easier for customers to do interactive log analytics, real-time application monitoring, and semantic and keyword searches. It can also be used as a vector engine that helps organizations build and augment GenAI applications without managing infrastructure (we’ll talk about this in future blogs). Additionally, the service provides a reliable, scalable infrastructure designed to handle massive data volumes.

Streamlined Data Movement: Fivetran's SAP ERP Integration with Databricks Explained

Join Kelly Kohlleffel from Fivetran as he demonstrates the seamless integration of SAP ERP data into the Databricks Data Intelligence Platform using Fivetran’s powerful data movement automation capabilities. Learn how the Fivetran SAP ERP for HANA connector effortlessly syncs your data to Databricks, making it ready for all data workloads. Discover how Fivetran utilizes Databricks’ features like Serverless and Unity Catalog to help you quickly build new data products and solutions with SAP ERP data efficiently in Databricks.

Fast-Track to Data Insights: Deliver Impactful Salesforce Sales Metrics to the Databricks Gold Layer

Join Kelly Kohlleffel from Fivetran in this demonstration that moves and transforms raw Salesforce data into impactful sales metrics in the Databricks Data Intelligence Platform. Learn how to set up a Salesforce to Databricks connector with Fivetran’s fully automated and fully managed data movement platform. Then watch how the new dataset in Databricks is automatically transformed from the Databricks bronze layer to the gold layer—making it analytics-ready and data product-ready in minutes.

Top 3 Benefits of Automated Analytics

Imagine transforming raw business data into actionable insights with minimal effort. This is the value proposition of automated analytics, a form of data analytics fast becoming more accessible among modern business intelligence (BI) and analytics software solution vendors. Independent software vendors (ISVs) and enterprise organizations at the cusp of investing in analytics struggle with manual data processes that are time-consuming and prone to errors.

Snowflake Expands Partnership with Microsoft to Improve Interoperability Through Apache Iceberg

Today we’re excited to announce an expansion of our partnership with Microsoft to deliver a seamless and efficient interoperability experience between Snowflake and Microsoft Fabric OneLake, in preview later this year. This will enable our joint customers to experience bidirectional data access between Snowflake and Microsoft Fabric, with a single copy of data with OneLake in Fabric.

Data Provenance vs. Data Lineage: Key Differences

Two related concepts often come up when data teams work on data governance: data provenance and data lineage. While they may seem similar at first glance, there are fundamental differences between the two concepts. Data provenance covers the origin and history of data, including its creation and modifications. On the other hand, data lineage tracks the data’s journey through various systems and processes, highlighting its flow and transformation across different data pipeline stages.

How to Build the Best Analytics Experience for Your SaaS Product

For any product team with a Software as a Service (SaaS) application, the significance of embedding capable, reliable analytics is well understood. But it’s important to remember how valuable these tools are for your users’ experience, too. Having fast access to data and easy ways to explore it is, as you know, key to monitoring business performance, finding trends, sharing insights, and improving your core product. Today’s business user also wants more when they use your product.

What is Data Observability? A Complete Guide

Data observability is a process that actively monitors an organization’s data for accuracy, health, and usefulness. It is the ability of an organization to have comprehensive visibility over its entire data landscape, including data pipelines, infrastructure, and applications. Data observability allows the organization to quickly identify, control, prevent, remediate, and fix data outages, all within agreed service level agreements (SLAs).