| <?xml version="1.0" encoding="utf-8"?> |
| <feed xmlns="http://www.w3.org/2005/Atom"> |
| <id>https://pinot.apache.org/blog</id> |
| <title>Apache Pinot: User-Facing Analytics</title> |
| <updated>2023-09-19T00:00:00.000Z</updated> |
| <generator>https://github.com/jpmonette/feed</generator> |
| <link rel="alternate" href="https://pinot.apache.org/blog"/> |
| <subtitle>Apache Pinot™ Blog</subtitle> |
| <icon>https://pinot.apache.org/img/favicon.ico</icon> |
| <entry> |
| <title type="html"><![CDATA[Announcing Apache Pinot 1.0™]]></title> |
| <id>Announcing Apache Pinot 1.0™</id> |
| <link href="https://pinot.apache.org/blog/2023/09/19/Annoucing-Apache-Pinot-1-0"/> |
| <updated>2023-09-19T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Introducing Apache Pinot 1.0 Release]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Segment Compaction for Upsert Enabled Tables in Apache Pinot]]></title> |
| <id>Segment Compaction for Upsert Enabled Tables in Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2023/08/04/segment-compaction-for-upsert-enabled-tables-in-apache-pinot-3f30657aa077"/> |
| <updated>2023-08-04T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[The blog post discusses the feature contribution of segment compaction to Apache Pinot project, addressing the issue of older records consuming unnecessary storage space. It explains the configuration and impact of segment compaction in freeing up storage. The author expresses gratitude and offers support for questions or feedback on segment compaction.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Star-Tree Index in Apache Pinot - Part 3 - Understanding the Impact in Real Customer Scenarios]]></title> |
| <id>Star-Tree Index in Apache Pinot - Part 3 - Understanding the Impact in Real Customer Scenarios</id> |
| <link href="https://pinot.apache.org/blog/2023/07/12/star-tree-index-in-apache-pinot-part-3-understanding-the-impact-in-real-customer"/> |
| <updated>2023-07-12T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[The blog post discusses how implementing a star-tree index significantly improved query performance for an AdTech platform by reducing latency. This index has also been successful in cybersecurity threat detection and multiplayer game leaderboard tracking, resulting in improved query performance and cost savings. Real production use cases showed a 95% to 99% improvement in query performance using StarTree Cloud for real-time analytics.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Real-Time Mastodon Usage with Apache Kafka, Apache Pinot, and Streamlit]]></title> |
| <id>Real-Time Mastodon Usage with Apache Kafka, Apache Pinot, and Streamlit</id> |
| <link href="https://pinot.apache.org/blog/2023/06/01/real-time-mastodon-usage-with-apache-kafka-apache-pinot-and-streamlit"/> |
| <updated>2023-06-01T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[The blog post discusses analyzing user activity and server popularity on Mastodon using Kafka Connect, Parquet, Seaborn, and DuckDB. It explores the potential of using Apache Pinot for real-time data streaming and creating a dashboard. The post provides instructions on ingesting Apache Avro messages into Pinot, creating a Pinot table, and querying the data.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[How to Ingest Streaming Data from Kafka to Apache Pinot™]]></title> |
| <id>How to Ingest Streaming Data from Kafka to Apache Pinot™</id> |
| <link href="https://pinot.apache.org/blog/2023/05/30/how-to-ingest-streaming-data-from-kafka-to-apache-pinot"/> |
| <updated>2023-05-30T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[The blog post explains how to use Apache Kafka topics in Apache Pinot to ingest streaming data, with step-by-step instructions provided for installation and setup. It focuses on ingesting Wikipedia events into Kafka and connecting it to Pinot to create a real-time table. The post highlights Pinot's capabilities in ingesting and transforming JSON data into OLAP tables and encourages reader engagement through the community Slack.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Change Data Capture with Apache Pinot - How Does It Work?]]></title> |
| <id>Change Data Capture with Apache Pinot - How Does It Work?</id> |
| <link href="https://pinot.apache.org/blog/2023/05/23/change-data-capture-with-apache-pinot-how-does-it-work"/> |
| <updated>2023-05-23T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[This blog post discusses the use of Change Data Capture (CDC) in Apache Pinot and the data format used in Debezium for efficient querying and analytics. It explains the elements of the format and its usage in indexing JSON fields. It also mentions the availability of CDC connectors in Debezium for various streaming systems.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Apache Pinot Tutorial for Getting Started - A Step-by-Step Guide]]></title> |
| <id>Apache Pinot Tutorial for Getting Started - A Step-by-Step Guide</id> |
| <link href="https://pinot.apache.org/blog/2023/05/18/apache-pinot-tutorial-for-getting-started-a-step-by-step-guide"/> |
| <updated>2023-05-18T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[This blog post is a guide to getting started with Apache Pinot, including installing and running the necessary components of a Pinot cluster. It also explains how to set up schemas, tables, and load data into Pinot, as well as how to run queries using the Pinot Data Explorer. The next article in the series will cover consuming event streaming data with Apache Pinot and Apache Kafka.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[StarTree Indexes in Apache Pinot Part-1 - Understanding the Impact on Query Performance]]></title> |
| <id>StarTree Indexes in Apache Pinot Part-1 - Understanding the Impact on Query Performance</id> |
| <link href="https://pinot.apache.org/blog/2023/05/16/star-tree-indexes-in-apache-pinot-part-1-understanding-the-impact-on-query-performance"/> |
| <updated>2023-05-16T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[The blog post explains the star-tree index in Apache Pinot and its benefits compared to traditional materialized views. By implementing a star-tree index, query performance significantly improved, reducing query latency from 1,513 ms to just 4 ms and drastically reducing disk reads by 99.999%.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Geospatial Indexing in Apache Pinot]]></title> |
| <id>Geospatial Indexing in Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2023/05/11/Geospatial-Indexing-in-Apache-Pinot"/> |
| <updated>2023-05-11T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Apache Pinot™ 0.12 - Consumer Record Lag]]></title> |
| <id>Apache Pinot™ 0.12 - Consumer Record Lag</id> |
| <link href="https://pinot.apache.org/blog/2023/03/30/Apache-Pinot-0-12-Consumer-Record-Lag"/> |
| <updated>2023-03-30T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[This post will explore a new API endpoint that lets you check how much Pinot is lagging when ingesting from Apache Kafka.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Apache Pinot™ 0.12 - Configurable Time Boundary]]></title> |
| <id>Apache Pinot™ 0.12 - Configurable Time Boundary</id> |
| <link href="https://pinot.apache.org/blog/2023/02/21/Apache-Pinot-0-12-Configurable-Time-Boundary"/> |
| <updated>2023-02-21T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[This post will explore the ability to configure the time boundary when working with hybrid tables.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Apache Pinot™ 0.11 - Deduplication on Real-Time Tables]]></title> |
| <id>Apache Pinot™ 0.11 - Deduplication on Real-Time Tables</id> |
| <link href="https://pinot.apache.org/blog/2023/01/29/Apache-Pinot-Deduplication-on-Real-Time-Tables"/> |
| <updated>2023-01-29T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Learn about the deduplication for the real-time tables feature in Apache Pinot]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Apache Pinot™ 0.11 - Pausing Real-Time Ingestion]]></title> |
| <id>Apache Pinot™ 0.11 - Pausing Real-Time Ingestion</id> |
| <link href="https://pinot.apache.org/blog/2022/11/28/Apache-Pinot-Pausing-Real-Time-Ingestion"/> |
| <updated>2022-11-28T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Learn about a feature that lets you pause and resume real-time data ingestion in Apache Pinot]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Apache Pinot™ 0.11 - Timestamp Indexes]]></title> |
| <id>Apache Pinot™ 0.11 - Timestamp Indexes</id> |
| <link href="https://pinot.apache.org/blog/2022/11/22/Apache-Pinot-Timestamp-Indexes"/> |
| <updated>2022-11-22T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Users write queries that use the datetrunc function to filter at a coarser grain of functionality. Unfortunately, this approach results in scanning data and time value conversion work that takes a long time at large data volumes. The timestamp index solves that problem!]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Apache Pinot™ 0.11 - Inserts from SQL]]></title> |
| <id>Apache Pinot™ 0.11 - Inserts from SQL</id> |
| <link href="https://pinot.apache.org/blog/2022/11/17/Apache Pinot-Inserts-from-SQL"/> |
| <updated>2022-11-17T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Explore the INSERT INTO clause, which makes ingesting batch data into Pinot as easy as writing a SQL query.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Apache Pinot™ 0.11 - How do I see my indexes?]]></title> |
| <id>Apache Pinot™ 0.11 - How do I see my indexes?</id> |
| <link href="https://pinot.apache.org/blog/2022/11/08/Apache Pinot-How-do-I-see-my-indexes"/> |
| <updated>2022-11-08T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[How you can work out which indexes are currently defined on a Pinot table]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[GapFill Function For Time-Series Datasets In Pinot]]></title> |
| <id>GapFill Function For Time-Series Datasets In Pinot</id> |
| <link href="https://pinot.apache.org/blog/2022/08/02/GapFill-Function-For-Time-Series-Datasets-In-Pinot"/> |
| <updated>2022-08-02T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Gapfilling functions in Pinot to provide the on-the-fly interpolation (filling the missing data) functionality to better handle time-series data.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Announcing Apache Pinot 0.10]]></title> |
| <id>Announcing Apache Pinot 0.10</id> |
| <link href="https://pinot.apache.org/blog/2022/04/04/Announcing-Apache-Pinot-0-10"/> |
| <updated>2022-04-04T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Learn more about the release of Apache Pinot 0.10 and all of new features that have been included in this version of the product.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Text analytics on LinkedIn Talent Insights using Apache Pinot]]></title> |
| <id>Text analytics on LinkedIn Talent Insights using Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2021/06/16/LinkedIn-TextAnalytics"/> |
| <updated>2021-06-16T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Introduction LinkedIn Talent Insights (LTI) is a platform that helps organizations understand the external labor market and their internal workforce, and enables the long term success of their employees]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Introduction to Geospatial Queries in Apache Pinot]]></title> |
| <id>Introduction to Geospatial Queries in Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2021/06/13/DevBlog-Geospatial"/> |
| <updated>2021-06-13T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Discuss the challenges of analyzing geospatial at scale and propose the geospatial support in Pinot.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Automating Merchant Live Monitoring with Real-Time Analytics - Charon]]></title> |
| <id>Automating Merchant Live Monitoring with Real-Time Analytics - Charon</id> |
| <link href="https://pinot.apache.org/blog/2021/04/29/Uber-Charon"/> |
| <updated>2021-04-29T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Focus on Uber’s real-time data platform components to build a tool called Charon to reduce impact of poor marketplace reliability on the merchants.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Deploying Apache Pinot at a Large Retail Chain]]></title> |
| <id>Deploying Apache Pinot at a Large Retail Chain</id> |
| <link href="https://pinot.apache.org/blog/2021/04/27/DevBlog-PinotInRetailChain"/> |
| <updated>2021-04-27T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches]]></title> |
| <id>Solving for the cardinality of set intersection at scale with Pinot and Theta Sketches</id> |
| <link href="https://pinot.apache.org/blog/2021/04/16/LinkedIn-Theta"/> |
| <updated>2021-04-16T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Introduction to Upserts in Apache Pinot]]></title> |
| <id>Introduction to Upserts in Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2021/04/08/DevBlog-UpsertsIntro"/> |
| <updated>2021-04-08T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Introduction to Pinot Upsert and explain why it’s exciting and how you can start using it.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Real-time Analytics with Presto and Apache Pinot]]></title> |
| <id>Real-time Analytics with Presto and Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2021/02/02/DevBlog-PrestoPinot"/> |
| <updated>2021-02-02T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Change Data Analysis with Debezium and Apache Pinot]]></title> |
| <id>Change Data Analysis with Debezium and Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2021/01/08/DevBlog-DebeziumCDC"/> |
| <updated>2021-01-08T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Pinot enters into a storied legacy of innovations that have emerged from one of the world’s largest online social networks. Over a few decades, the Silicon Valley tech giant has helped hundreds of millions of people around the world navigate their careers.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[From Lambda to Lambda-less Lessons learned]]></title> |
| <id>From Lambda to Lambda-less Lessons learned</id> |
| <link href="https://pinot.apache.org/blog/2020/12/01/LinkedIn-Lamda"/> |
| <updated>2020-12-01T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[The Lambda architecture has become a popular architectural style that promises both speed and accuracy in data processing by using a hybrid approach of both batch processing and stream processing methods.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Operating Apache Pinot at Uber Scale]]></title> |
| <id>Operating Apache Pinot at Uber Scale</id> |
| <link href="https://pinot.apache.org/blog/2020/10/20/Uber-Operating"/> |
| <updated>2020-10-20T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Present details of this platform and how it fits in Uber’s ecosystem. Explain how uber scaled from a few use cases to a multi-cluster powering hundreds of use cases for querying terabyte-scale data with millisecond latencies.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Deep Analysis of Russian Twitter Trolls]]></title> |
| <id>Deep Analysis of Russian Twitter Trolls</id> |
| <link href="https://pinot.apache.org/blog/2020/10/16/DevBlog-TwitterTrollAnalysis"/> |
| <updated>2020-10-16T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Show you how to use Apache Pinot and Superset to analyze 3 million tweets by the Internet Research Agency (IRA) open-sourced by FiveThirtyEight.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Leverage Plugins to Ingest Parquet Files from S3 in Pinot]]></title> |
| <id>Leverage Plugins to Ingest Parquet Files from S3 in Pinot</id> |
| <link href="https://pinot.apache.org/blog/2020/08/08/DevBlog-IngestPlugins"/> |
| <updated>2020-08-08T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Pinot is its pluggable architecture. The plugins make it easy to add support for any third-party system which can be an execution framework, a filesystem, or input format.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Achieving 99th percentile latency SLA using Apache Pinot]]></title> |
| <id>Achieving 99th percentile latency SLA using Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2020/08/08/DevBlog-SLAApps"/> |
| <updated>2020-08-08T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[How users can build critical site-facing analytical applications requiring high throughput and strict p99th query latency SLA]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Monitoring Apache Pinot with JMX, Prometheus and Grafana]]></title> |
| <id>Monitoring Apache Pinot with JMX, Prometheus and Grafana</id> |
| <link href="https://pinot.apache.org/blog/2020/08/08/DevBlog-PinotMonitoring"/> |
| <updated>2020-08-08T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Blog gives an overview of our use of Apache Pinot to solve some of biggest challenges around Data Analytics in Large Retail Chain]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Utilize UDFs to Supercharge Queries in Apache Pinot]]></title> |
| <id>Utilize UDFs to Supercharge Queries in Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2020/08/08/DevBlog-ScalarUDFs"/> |
| <updated>2020-08-08T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Scalar Functions that allow users to write and add their functions as a plugin.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Building a culture around metrics and anomaly detection]]></title> |
| <id>Building a culture around metrics and anomaly detection</id> |
| <link href="https://pinot.apache.org/blog/2020/07/28/DevBlog-AnomalyDetection"/> |
| <updated>2020-07-28T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[ThirdEye as a system is a platform that allows you to integrate your metrics (quantitative information) with events (knowledge or qualitative information) and combine the two so you can distinguish between meaningless anomalies and those ones that matter.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Moving developers up the stack with Apache Pinot]]></title> |
| <id>Moving developers up the stack with Apache Pinot</id> |
| <link href="https://pinot.apache.org/blog/2020/07/28/DevBlog-DevUpStack"/> |
| <updated>2020-07-28T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Pinot enters into a storied legacy of innovations that have emerged from one of the world’s largest online social networks. Over a few decades, the Silicon Valley tech giant has helped hundreds of millions of people around the world navigate their careers.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Bridging batch and stream processing for the Recruiter usage statistics dashboard]]></title> |
| <id>Bridging batch and stream processing for the Recruiter usage statistics dashboard</id> |
| <link href="https://pinot.apache.org/blog/2020/07/14/LinkedIn-BatchRealtime"/> |
| <updated>2020-07-14T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Building LinkedIn Talent Insights to democratize data-driven decision making]]></title> |
| <id>Building LinkedIn Talent Insights to democratize data-driven decision making</id> |
| <link href="https://pinot.apache.org/blog/2020/06/29/LinkedIn-TalentInsight"/> |
| <updated>2020-06-29T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Monitoring business performance data with ThirdEye smart alerts]]></title> |
| <id>Monitoring business performance data with ThirdEye smart alerts</id> |
| <link href="https://pinot.apache.org/blog/2020/06/25/LinkedIn-SmartAlerts"/> |
| <updated>2020-06-25T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Using Apache Pinot and Kafka to Analyze GitHub Events]]></title> |
| <id>Using Apache Pinot and Kafka to Analyze GitHub Events</id> |
| <link href="https://pinot.apache.org/blog/2020/04/10/DevBlog-AnalyzeGitEvents"/> |
| <updated>2020-04-10T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Show you how Pinot and Kafka can be used together to ingest, query, and visualize event streams sourced from the public GitHub API.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Analyzing anomalies with ThirdEye]]></title> |
| <id>Analyzing anomalies with ThirdEye</id> |
| <link href="https://pinot.apache.org/blog/2020/02/20/LinkedIn-Thirdeye"/> |
| <updated>2020-02-20T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Engineering SQL Support on Apache Pinot at Uber]]></title> |
| <id>Engineering SQL Support on Apache Pinot at Uber</id> |
| <link href="https://pinot.apache.org/blog/2020/01/15/Pinot-Presto-SQL"/> |
| <updated>2020-01-15T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Talks about solution that linked Presto, a query engine that supports full ANSI SQL, and Pinot, a real-time OLAP (online analytical processing) datastore.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Auto-tuning Pinot real-time consumption]]></title> |
| <id>Auto-tuning Pinot real-time consumption</id> |
| <link href="https://pinot.apache.org/blog/2019/07/11/LinkedIn-AutoTune"/> |
| <updated>2019-07-11T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Focus on using large set intersection cardinality approximations with Apache Pinot and Theta Sketches, which allow us to efficiently figure out the unique size of a targeted audience when factoring in multiple criteria of an advertising campaign.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Star-tree index - Powering fast aggregations on Pinot]]></title> |
| <id>Star-tree index - Powering fast aggregations on Pinot</id> |
| <link href="https://pinot.apache.org/blog/2019/06/14/LinkedIn-StarTree"/> |
| <updated>2019-06-14T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Introduced Star-Tree index to utilize the pre-aggregated documents in a smart way that achieves low query latencies, while using the storage space efficiently.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform]]></title> |
| <id>Introducing ThirdEye - LinkedIn’s Business-Wide Monitoring Platform</id> |
| <link href="https://pinot.apache.org/blog/2019/01/09/LinkedIn-IntroThirdEye"/> |
| <updated>2019-01-09T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[ThirdEye is a comprehensive platform for real-time monitoring of metrics that covers a wide variety of use-cases.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Engineering Restaurant Manager - UberEATS Analytics Dashboard]]></title> |
| <id>Engineering Restaurant Manager - UberEATS Analytics Dashboard</id> |
| <link href="https://pinot.apache.org/blog/2017/09/17/Restaurant-Manager"/> |
| <updated>2017-09-17T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Restaurant Manager is a comprehensive analytics dashboard and pipeline for our restaurant partners. In this article, we discuss how we architected this analytics platform and its robust data pipeline.]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics]]></title> |
| <id>Open Sourcing Pinot - Scaling the Wall of Real-Time Analytics</id> |
| <link href="https://pinot.apache.org/blog/2015/06/10/Open-Sourcing-Pinot"/> |
| <updated>2015-06-10T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[Introducing Pinot which allow to slice and dice across billions of rows in real-time across a wide variety of products]]></summary> |
| </entry> |
| <entry> |
| <title type="html"><![CDATA[A Brief History of Scaling LinkedIn]]></title> |
| <id>A Brief History of Scaling LinkedIn</id> |
| <link href="https://pinot.apache.org/blog/2015/05/16/LinkedIn-Scaling"/> |
| <updated>2015-05-16T00:00:00.000Z</updated> |
| <summary type="html"><![CDATA[LinkedIn started in 2003 with the goal of connecting to your network for better job opportunities. It had only 2,700 members the first week. Fast forward many years, and LinkedIn’s product portfolio, member base, and server load has grown tremendously.]]></summary> |
| </entry> |
| </feed> |
