In an era of dried-up funding and Data Lakehouse vendor supremacy, Redpanda is going against the grain. The company just secured a $100 million Series C funding

GPUs are now being put to the test in the three fastest developing applications in today’s tech ecosystem.

The promise of Data Lakehouses is in their capabilities for structured and unstructured data, all in a centralized solution using Apache Iceberg and MinIO.

As we sit down for this exclusive interview, Leonid offers a rare glimpse into the intricate process of weaving the digital fabric that shapes our lives.

Draw a line between Data Analytics and Business Intelligence — compare their scope, capabilities, use cases, and potential for business decision-making.

Explore the pros and cons of industry-leading blockchain analytic tools, examining how each solution handles data across the blockchain network.

Discover how to boost Apache Spark's query efficiency using data sketches for fast counts and intersections in large datasets. Essential for data pros!

Explore 5 ways Decision Support Systems transform enterprise decision-making in 2024.

Discover how dashboard software revolutionizes businesses—enhancing efficiency, empowering decisions, and driving cost-effective insights in 2024.

Learn everything you need to know about Big Data Analytics via these 89 free HackerNoon stories.

We will guide you through a specific dataset, demonstrating how to implement the library and interpret results.

Cross-cluster replication (CCR) in Apache Doris is proven to be fast, stable, and easy to use. It secures a real-time data synchronization latency of 1 second.

NetEase has replaced Elasticsearch and InfluxDB with Apache Doris in its monitoring and time series data analysis platforms, respectively

Discover an efficient approach to implementing the sliding window median algorithm for big data analytics.

Apache Doris introduces Auto Partition in V2.1.0. It has simplified DDL and partition management and is useful in large-scale data processing

Rahul Chaturvedi is a Staff Software Engineer at Uber Technologies Inc. He has been at the forefront of optimizing one of the world's largest Kafka deployments.

Discover how GPU acceleration is reshaping data processing, offering unparalleled speed and efficiency for AI and big data analytics.

In this guide, we will explore data analytics using **PyArrow**, a powerful library designed for efficient in-memory data processing with columnar storage.

Discover how Parquet's columnar storage optimizes data processing for faster queries, better compression, and efficient aggregation in big data environments.

If we can answer what, where, when and how of data processing we can build a very robust stream processing pipeline using Apache Flink and Apache Beam.