Originally published on trino.io: https://trino.io/blog/2023/07/21/trino-fest-2023-alluxio-recap.html By 2025, there will be 100 zetabytes stored in the cloud. That’s 100,000,000,000,000,000,000,000 bytes – a huge, eye-popping number. But only about 10% of that data is actually used on a regular basis. At Uber, for example, only 1% of their disk space is used for 50% of the data they access … Continued
Your 🐰 queries are slow 🐢 … you’re frustrated 😩 … Don’t let suboptimal Trino performance hold you back any longer! Unlock the full potential of Trino and transform your data analytics game. Discover the secrets behind Trino’s query engine and learn how to overcome bottlenecks to achieve⚡ blazing-fast query performance. In this comprehensive guide, … Continued
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.
This presentation focuses on how Alluxio helps the big data analytics stack to be cloud-native. The trending Cloud object storage systems provide more cost-effective and scalable storage solutions but also different semantics and performance implications compared to HDFS. Applications like Spark or Presto will not benefit from the node-level locality or cross-job caching when retrieving data from the cloud object storage. Deploying Alluxio to access cloud solves these problems because data will be retrieved and cached in Alluxio instead of the underlying cloud or object storage repeatedly.
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.
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.
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.
Alluxio is the data orchestration platform to unify data silos across heterogeneous environments. This is the last article in a series to give you the basics of Alluxio’s architecture and solution.
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.