Setting Up Presto with Apache Superset: Hands-On Guide
PrestoDB, an open-source distributed SQL query engine, allows you to query data from multiple disparate sources. When combined with Apache Superset, an| PrestoDB
Recent analyses indicate that approximately 90% of the world’s data has been generated within the past two years, and according to IDC, the volume of data stored globally is doubling approximately every four years. That’s the projection from IDC’s Global DataSphere Forecast, and it underscores the urgent need for businesses to make sense of rapidly … Continue reading Top Databricks Use Cases That Are Changing the Game in Business Analytics The post Top Databricks Use Cases That Are Ch...| Credencys Solutions Inc.
PrestoDB, an open-source distributed SQL query engine, allows you to query data from multiple disparate sources. When combined with Apache Superset, an| PrestoDB
Reading Time: 4minutesImagine you’re a financial analyst at a pension fund, racing against the clock to deliver a major corporate client’s portfolio breakdown before fiscal year end. You’re juggling CRM data, financial market reports, and a tangle of Excel exports. Or picture... The post Beyond the Lakehouse: Denodo’s RAG-Driven Data Revolution appeared first on Data Management Blog - Data Integration and Modern Data Management Articles, Analysis and Information.| Data Management Blog – Data Integration and Modern Data Management Articles...
Learn what Lakehouse AI is, its key features, benefits, and real-world use cases to streamline data and accelerate AI model deployment.| HatchWorks AI
This tutorial provides a comprehensive guide to building an Open Data Lakehouse from scratch, a modern and flexible data architecture solution. Open Data| PrestoDB
This post is the second in a series on data lakehouses, expanding on the key insights shared in “Data lakehouse benefits: Why enterprises are choosing this modern data architecture”. In that article, we explored the reasons behind the rapid adoption of lakehouse platforms and how they blend the flexibility of data lakes with the governance […] The post Data lakehouse strategy: Build a foundation for real-time, AI-ready insights appeared first on The Quest Blog.| The Quest Blog
If AI hasn’t blown our minds yet, Snowflake Summit 2025 made it clear what’s coming soon will. I flew into San Francisco expecting some solid updates and left feeling like I’d seen the blueprint for the next era of enterprise AI. The conference radiated with ambition, energy and the promise of “breathtaking” models that unlock […] The post Snowflake Summit: Key takeaways from a week of AI, data and dog robots appeared first on The Quest Blog.| The Quest Blog
Stay updated with the latest PIM and MDM trends. Get insights into how these technologies fuel the growth of your business.| Credencys Solutions Inc.
A new report reveals that most organizations plan to adopt data lakehouses as their primary analytics architecture by 2027.| CDInsights
With Dremio and Apache Iceberg, managing partitioning and optimizing queries becomes far simpler and more effective. By leveraging Reflections, Incremental Reflections, and Live Reflections, you can maintain fresh data, reduce the complexity of partitioning strategies, and optimize for different query plans without sacrificing performance. Using Dremio’s flexible approach, you can balance keeping raw tables simple and ensuring that frequently run queries are fully optimized.| Dremio
Maintaining an Apache Iceberg Lakehouse involves strategic optimization and vigilant governance across its core components—storage, data files, table formats, catalogs, and compute engines. Key tasks like partitioning, compaction, and clustering enhance performance, while regular maintenance such as expiring snapshots and removing orphan files helps manage storage and ensures compliance. Effective catalog management, whether through open-source or managed solutions like Dremio's Enterprise ...| Dremio
Explore a comparative analysis of Apache Iceberg and other data lakehouse solutions. Discover unique features and benefits to make an informed choice.| Dremio
Integrating Snowflake with the Dremio Lakehouse Platform offers a powerful combination that addresses some of the most pressing challenges in data management today. By unifying siloed data, optimizing analytics costs, enabling self-service capabilities, and avoiding vendor lock-in, Dremio complements and extends the value of your Snowflake data warehouse.| Dremio
Dremio enables directly serving BI dashboards from Apache Druid or leveraging Apache Iceberg tables in your data lake. This post will explore how Dremio's data lakehouse platform simplifies your data delivery for business intelligence by doing a prototype version that can run on your laptop.| Dremio
Moving data from source systems like MySQL to a dashboard traditionally involves a multi-step process: transferring data to a data lake, moving it into a data warehouse, and then building BI extracts and cubes for acceleration. This process can be tedious and costly. However, this entire workflow is simplified with Dremio, the Data Lakehouse Platform. Dremio enables you to directly serve BI dashboards from MySQL or leverage Apache Iceberg tables in your data lake.| Dremio
The latest release of the Vertica analytical database, now OpenTextTM VerticaTM includes a lot of features that Vertica customers have been eagerly awaiting like: Resharding the database as needed Rollback snapshots that capture a moment in time without a whole other data copy Workload routing so you can automate directing specific queries to just the right compute for that type of job. And more ...| OpenText™ Vertica™
