From Oracle to MongoDB: How to Modernize Your Tech Stack for Real-Time AI Decisioning

Playlists for every mood and occasion. Media recommendations grouped by the most niche theme from your watch history. Sophisticated ad algorithms that optimize pay-per-click ads for the customer experience. Whether you call them digital-native, disruptors, or just tech giants, the likes of Spotify, Netflix, and Amazon have long made uncannily personal experiences a key part of their differentiation or business models.

LLM Evaluation and Testing for Reliable AI Apps

As LLMs become central to AI-driven products like copilots and customer support chatbots, data science teams need to ensure the LLM performs well for the use case. The process of LLM evaluation ensures reliability, safety and performance in production AI systems. In this guide, we explore how to approach evaluations across development and production lifecycles, what frameworks to use, and how the integration between open-source MLRun and Evidently AI enables more scalable, structured testing.

Counties Energy Powers The Future With Snowflake's AI Data Cloud

With the help of its technology partner, dataengine, Counties Energy was able to implement the Snowflake AI Data Cloud platform, and now relies on it as the foundational tool for managing the company's operations and growth. Snowflake serves as a solid data foundation that is enabling more employees to make data-driven decisions by using natural language to query complex data.

Maximize Your Translytical Write Back Capabilities in Power BI

When Microsoft released translytical task flows with write back, Power BI took a big leap. This is just the beginning of Microsoft enhancing write back capabilities within Power BI. Let’s take a look at where this currently stands, and where it is going. Microsoft’s translytical task flows bring native write back to Power BI. That’s a massive shift: Power BI becomes a two-way street—you can finally write data back, not just read it.

Get More Out of Your Data Lakehouse With Trino

Let’s face it. Data lakehouses are the new normal, but that does not mean they are easy to use. Apache Iceberg gives you version control, schema evolution, and fine-grained partitioning. Trino lets you query it all with blazing speed. When it is time to plug that into your BI tools or analytics pipelines, things often grind to a halt. The problem is not your data or your engine. It is your connector. Architecting a data lakehouse is one thing. Getting it to actually perform is another.

Want content marketing buy-in? Do this first

Amanda Natividad, VP of Marketing at SparkToro, shares how to win over CFOs, sales leaders, and legal – by making content that meets their goals, not just yours. Build trust Create assets they ask for (hello, case studies ) Make legal’s life easier = faster approvals “It’s not always ROI or VOI… sometimes it’s just showing you value their time.” Databox is Modern BI for teams that need answers now. It offers the best of BI, without the complicated setup, steep price, or long learning curve.

ClickHouse ETL Tools: Fast Column-Store Integration Options

ClickHouse has emerged as the world's fastest analytical database, processing billions of rows per second for companies like Uber, Cloudflare, and Spotify. This open-source columnar database excels at real-time analytics, but its unique architecture creates specific ETL challenges that traditional data integration tools struggle to address effectively.

Apache Druid ETL Tools: Streaming & Batch Connectors Reviewed

Apache Druid has emerged as the go-to solution for organizations requiring lightning-fast analytics on massive datasets. According to the Apache Druid ingestion documentation, this distributed, column-oriented database combines concepts from data warehouses, time-series databases, and search systems to deliver sub-second query performance on trillions of rows.

Introducing Private Network Interface: Secure Private Networking on AWS for 50% Less

This is the second post in our series exploring the architectural innovations that make Confluent Cloud more cost-effective at scale. Building on our previous post about the operational complexities of Apache Kafka and our cloud-native architecture's solutions, we'll now dive into how we solved a core challenge for any data streaming workload: high cloud networking costs.

Lenses K2K | See the universal Kafka to Kafka replicator in action

Lenses K2K is a new Kafka to Kafka replicator that gives any user the power to easily and reliably share real-time data across their business. Why is it different to the likes of MirrorMaker2, Confluent Cluster Linking and AWS Replicator? Vendor agnostic: Replicate data to and from any cluster or vendor, from Confluent Cloud and Redpanda to AWS and Azure Event Hubs. Self service: Full self-service capabilities to empower teams to replicate data, all while governed by multi-cluster Identity and Access Management and auditing.