Introduction To Apache Pinot

Imagine you run an online store and want to track how many people are buying a product right now. You don’t want to wait for a report the next day—you need the data instantly! This is where Apache Pinot comes in. It helps businesses see their data in real-time, allowing them to make quick decisions.

 

This guide will introduce Apache Pinot in a simple and beginner-friendly way, help you understand what it does, how it works, and why it is useful.

 

1. What is Apache Pinot?

Apache Pinot is a real-time Distributed OLAP database designed to answer questions instantly. It is used when companies need to analyze a lot of data very quickly.

 

For example:

·      E-commerce Websites: Track live sales and customer behavior.

·      Social Media: See trending hashtags and popular posts in real time.

·      Ride-Sharing Apps: Monitor how many drivers are available in a city right now.

 

It is super-fast and helps businesses to make decisions on the spot instead of waiting hours or days.

 

2. Why Use Apache Pinot?

Apache Pinot is perfect for real-time dashboards that show live data updates. Here’s why people love using it:

 

·      Instant Answers: It can process millions of records in less than a second.

·      Handles Large Data: Works even when there is a massive amount of data.

·      Easy to Connect: Works with Kafka, databases, and cloud storage.

·      Great for Dashboards: Used with Grafana, Superset, and other visualization tools.

 

3. How Does Apache Pinot Work?

Think of Apache Pinot as a super-smart calculator that can find answers quickly from a huge pile of data. Here’s how it works:

 

·      Data Comes In: Information is sent from websites, apps, or files.

·      Pinot Stores It: The data is organized neatly for quick searching.

·      User Asks a Question: Like “How many people bought shoes in the last 10 minutes?”

·      Pinot Finds the Answer: It scans the data instantly and gives a result.

 

4. Who Uses Apache Pinot?

Big companies use Apache Pinot to track real-time trends. Some examples:

 

·      Companies like Uber: To see how many drivers are active right now.

·      Companies like LinkedIn: To check which job posts are trending.

·      Companies like Amazon: To track which products are selling the fastest.

 

5. Apache Pinot Vs Apache Spark

Apache Pinot and Apache Spark are both big data tools, but they serve different purposes. Following table summarizes the same.

 

Feature	Apache Pinot	Apache Spark
Type	Real-time OLAP database	Big data processing engine
Best For	Fast analytics & dashboards	Batch & stream processing
Latency	Sub-second queries	Seconds to minutes
Use Case	Live dashboards, real-time insights	Data transformation, ML, ETL
Data Storage	Stores data for real-time queries	Processes data but doesn’t store it
Query Language	SQL-like queries	Uses Spark SQL, Scala, Python
Streaming Support	Yes (works with Kafka)	Yes (Spark Streaming)
Joins & Complex Computations	Limited	Supports heavy computations & joins

 

Can Apache Pinot and Spark Work Together?

Yes! You can use Apache Spark to preprocess data and Apache Pinot to query it in real time.

 

For example:

·      Spark processes raw data from different sources (logs, files, databases).

·      Pinot loads this processed data for real-time analytics & dashboards.

 

6. Apache Pinot Vs BigQuery

Apache Pinot and Google BigQuery are both powerful tools for analyzing large amounts of data, but they serve different purposes.

 

Following table summarizes the same.

 

Feature	Apache Pinot	BigQuery
Type	Real-time OLAP datastore	Cloud-based data warehouse
Best For	Fast real-time analytics & dashboards	Ad-hoc & batch analytics on huge datasets
Latency	Sub-second queries (real-time)	Seconds to minutes (optimized for large queries)
Use Case	User-facing analytics, dashboards, monitoring	Deep analytics, data warehousing, reporting
Data Storage	Stores optimized real-time data	Stores huge amounts of historical data
Query Language	SQL-like queries	Standard SQL
Streaming Support	Yes (Kafka, Kinesis, Pulsar)	Yes (via Pub/Sub, Dataflow)
Infrastructure	Self-hosted (on-premise or cloud)	Fully managed by Google Cloud
Cost	Open-source (self-hosted cost)	Pay-per-query pricing
Joins & Complex Computations	Limited support	Advanced analytics, joins, ML support

 

Can Apache Pinot and BigQuery Work Together?

Yes! You can use BigQuery for deep historical analysis and Apache Pinot for real-time queries.

 

For example:

·      BigQuery stores long-term data for deep insights and historical reports.

·      Pinot handles real-time queries on fresh data for live dashboards.

 

7. Apache Pinot Vs Apache Druid

Apache Pinot and Apache Druid are both real-time OLAP (Online Analytical Processing) data stores designed for fast analytics, but they have some differences in how they handle data ingestion, query performance, and architecture.

 

Following table summarizes the same.

 

Feature	Apache Pinot	Apache Druid
Type	Real-time OLAP datastore	Real-time OLAP datastore
Best For	User-facing real-time analytics & dashboards	Operational analytics & time-series analysis
Latency	Sub-second query response	Sub-second query response
Use Case	Ad-hoc queries, real-time dashboards, anomaly detection	Time-series analytics, event-driven analytics. Ex: logs, metrics, events etc.,
Data Storage	Columnar storage with indexing	Columnar storage with time-based partitioning
Query Language	SQL-like queries	Druid SQL & JSON-based queries
Streaming Support	Yes (Kafka, Kinesis, Pulsar)	Yes (Kafka, Kinesis)
Indexing	Forward index, inverted index, star-tree index	Bitmap index, segment-based indexing
Complex Joins & Aggregations	Supports joins using Presto/Trino	Limited joins, optimized for time-series aggregations
Infrastructure	Self-hosted or cloud	Self-hosted or cloud

        

8. Apache Pinot Vs Trino

Apache Pinot and Apache Trino (formerly PrestoSQL) are both powerful analytics tools, but they serve different purposes.

 

Apache Pinot is a real-time OLAP (Online Analytical Processing) datastore optimized for low-latency analytics on fresh data.

 

Apache Trino is a distributed SQL query engine designed for querying data from multiple sources (data lakes, warehouses, and databases) efficiently.

 

Following table summarizes the same.

 

Feature	Apache Pinot	Apache Trino
Type	Real-time OLAP datastore	Distributed SQL query engine
Best For	Fast real-time analytics & dashboards	Querying large-scale data across multiple sources
Latency	Sub-second queries	Depends on the data source (can be seconds to minutes)
Data Storage	Columnar storage with advanced indexing	Does not store data, queries external sources
Query Language	SQL-like queries	Full ANSI SQL
Joins & Complex Queries	Limited joins (optimized for fast lookups)	Full join support across multiple datasets
Streaming Support	Yes (Kafka, Kinesis, Pulsar)	No direct support (queries existing stores with streaming data)
Infrastructure	Self-hosted or cloud	Self-hosted or cloud

Can Apache Pinot and Apache Trino Work Together?

Yes! Apache Trino can query Apache Pinot as a data source. This allows you to:

·      Use Trino for complex joins and federated queries across multiple systems.

·      Use Pinot for sub-second analytics on real-time data.

·      Combine historical and real-time data for a hybrid analytics solution.            

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