Real-Time Analytics: Going Beyond Stream Processing With Apache Pinot

Streaming systems form the backbone of the modern data pipeline as the stream processing capabilities provide insights on events as they arrive. But what if we want to go further than this and execute analytical queries on this real-time data? That’s where Apache Pinot comes in.

OLAP databases used for analytical workloads traditionally executed queries on yesterday’s data with query latency in the 10s of seconds. The emergence of real-time analytics has changed all this and the expectation is that we should now be able to run thousands of queries per second on fresh data with query latencies typically seen on OLTP databases.

Apache Pinot is a realtime distributed OLAP datastore, which is used to deliver scalable real time analytics with low latency. It can ingest data from streaming sources like Kafka, as well as from batch data sources (S3, HDFS, Azure Data Lake, Google Cloud Storage), and provides a layer of indexing techniques that can be used to maximize the performance of queries.

Come to this talk to learn how you can add real-time analytics capability to your data pipeline.

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Modernize your analytics workloads with NetApp and Alluxio

Imagine as an IT leader having the flexibility to choose any services that are available in public cloud and on premises. And imagine being able to scale your storage for your data lakes with control over data locality and protection for your organization. With these goals in mind, NetApp and Alluxio are joining forces to help our customers adapt to new requirements for modernizing data architecture with low-touch operations for analytics, machine learning, and artificial intelligence workflows.

Simplify and Accelerate Your Geo-Distributed Analytics Platform at Scale

Today, many organizations are running a multitude of data-driven applications and data platforms that span multiple geographic regions and across heterogeneous environments – public, private, hybrid, or multi-cloud. Further, the trend of separating compute resources from storage resources makes it easier to scale compute and storage independently, allowing organizations to keep up with new trends in data analytics and AI. In response, more organizations are modernizing their data platforms to meet their needs.

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Spark + Alluxio Overview | Pair Spark with Alluxio to Modernize Your Data Platform

By bringing Alluxio together with Spark, you can modernize your data platform in a scalable, agile, and cost-effective way.  In this post, we provide an overview of the Spark + Alluxio stack. We explain the architecture, discuss real-world examples, describe deployment models, and showcase performance and cost benchmarking.

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Architecting a Heterogeneous Data Platform Across Clusters, Regions, and Clouds

Data platform teams are increasingly challenged with accessing multiple data stores that are separated from compute engines, such as Spark, Presto, TensorFlow or PyTorch. Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access. Alluxio enables you to embrace the separation of storage from compute and use Alluxio data orchestration to simplify adoption of the data lake and data mesh paradigms for analytics and AI/ML workloads.

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Architecting a Heterogeneous Data Platform Across Clusters, Regions, and Clouds

Alluxio Product School *

Whether your data is distributed across multiple datacenters and/or clouds, a successful heterogeneous data platform requires efficient data access. Alluxio enables you to embrace the separation of storage from compute and use Alluxio data orchestration to simplify adoption of the data lake and data mesh paradigms for analytics and AI/ML workloads.

What’s New in Alluxio 2.7: Enhanced Scalability, Stability and Major Improvements in AI/ML Training Efficiency

With this release, Alluxio has strengthened its position as a de-facto data unification and acceleration solution in data analytics and machine learning pipelines. The solution is optimized to support Spark, Presto, Tensorflow, and PyTorch, and is available on multiple cloud platforms such as AWS, GCP, and Azure Cloud, and also on Kubernetes in private data centers or public clouds.