How Apache Pinot Serves Up Real-Time Personalization at Scale
With online transactions expected to represent almost 25% of retail sales by 2025, the need to personalize digital interactions with customers has moved from being a luxury to a necessity. Real-time analytics enables companies to deliver personalized content, recommendations, and services on the spot. The open-source Apache Pinot distributed data store for real-time analytics is emerging as an indispensable tool for achieving this goal.
There is overwhelming evidence of personalization’s payoff. Adobe reported that one-third of organizations spend more than half their budget personalizing digital content and 95% will maintain or increase personalization budgets over the next five years. IndustryArc estimates the market for recommendation engines, which are popular vehicles to personalize e-commerce interactions, has grown more than 10-fold from $1.14 billion in 2018 to $12 billion by 2025.
Why “real-time” matters when it comes to personalization
Time is a critical factor in personalization. Users are accustomed to moving swiftly between digital platforms, meaning businesses must capture and analyze their behavioral patterns on the fly to deliver relevant experiences while they have a person’s attention.
Real-time analytics enables platforms to deliver content tailored to users’ current behavior and preferences. Examples include e-commerce sites that recommend products based on a person’s browsing behavior and streaming video sites that queue up content relevant to what the viewer watches. Such automated actions need to be triggered in milliseconds.
Apache Pinot: Ideal for personalization use cases
Apache Pinot is uniquely suited to power personalization through real-time analytics due to its architecture, performance, and flexibility. The project originated at LinkedIn, where it powered the breakthrough capability to allow members to see who is viewing their profile instantly. Pinot is built to handle high-throughput, low-latency queries on massive data sets, an essential feature when a few milliseconds can make the difference between a conversion and a lost prospect.
Apache Pinot can ingest data from various batch and streaming sources, such as Apache Kafka, Amazon Kinesis, Apache Pulsar and Apache Hadoop, and make it immediately available for queries. This is distinct from transactional databases, which require a record to be written before it can be queried. Real-time data ingestion allows businesses to act on data as it streams into the database, allowing a website, for example, to show ads related to content visible on a user’s screen at that moment.
What makes Pinot uniquely suited for personalization?
Personalization at scale requires handling vast amounts of data that millions of users generate. Pinot can scale vertically by adding CPU and memory to each node and horizontally by expanding the number of nodes in the cluster. This makes it capable of processing highly variable volumes of data without compromising performance. Businesses can maintain the same personalization quality as their user base grows.
Pinot is also distinctive in its ability to support pluggable indexing algorithms such as forward and inverted indexing, text and JSON indexing, geospatial and vector indexing and the unique star-tree indexing. Pluggable indexing allows indexing strategies to be customized to specific data access patterns. For example, timestamp indexes support filtering, aggregation, and join operations, often required for advanced personalization algorithms. This flexibility ensures that queries are executed as efficiently as possible, reduces latency and improves overall performance. It also makes Pinot appropriate for a wide variety of use cases.
Real-time personalization can be resource-intensive. Apache Pinot’s columnar storage format and efficient indexing reduce the resources required for querying large data sets to reduce costs.
Leading digital news organization improved their SLA with Pinot and StarTree
BurdaForward, a leading German digital news organization with more than 40 million users, switched from AWS Elasticsearch to the Pinot-powered StarTree Cloud to consolidate and integrate data streaming from its family of 16 publishers and 100 applications. It built a dashboard that gives editors immediate insight into trending news, popular pages and traffic sources. This lets them quickly identify articles to promote on their home pages to keep readers engaged.
The company achieved a service level agreement of less than 10 milliseconds and reduced query failure rates to nearly zero. StarTree Cloud’s tiered storage architecture allowed the publisher to store far more data than possible to analyze trends over longer periods. Data engineers used Pinot’s pluggable indexes to apply StarTree Cloud to different use cases. Advertisers get timely data that integrates seamlessly with existing infrastructure, making it a cost-effective and efficient solution for their analytics needs.
Apache Pinot empowers businesses to create experiences that foster engagement, loyalty, and growth by enabling dynamic content delivery, instant feedback loops, and scalable personalization strategies.
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