DataBend vs Apache Pinot
A detailed comparison
Compare DataBend and Apache Pinot for time series and OLAP workloads
Learn About Time Series DatabasesChoosing the right database is a critical choice when building any software application. All databases have different strengths and weaknesses when it comes to performance, so deciding which database has the most benefits and the most minor downsides for your specific use case and data model is an important decision. Below you will find an overview of the key concepts, architecture, features, use cases, and pricing models of DataBend and Apache Pinot so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how DataBend and Apache Pinot perform for workloads involving time series data, not for all possible use cases. Time series data typically presents a unique challenge in terms of database performance. This is due to the high volume of data being written and the query patterns to access that data. This article doesn’t intend to make the case for which database is better; it simply provides an overview of each database so you can make an informed decision.
DataBend vs Apache Pinot Breakdown
 |
 |
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| Database Model | Data warehouse |
Columnar database |
| Architecture | DataBend can be run on your own infrastructure or using a managed service. It is designed as a cloud native system and is built to take advantage of many of the services available in cloud providers like AWS, Google Cloud, and Azure. |
Pinot can be deployed on-premises, in the cloud, or using a managed service |
| License | Apache 2.0 |
Apache 2.0 |
| Use Cases | Data analytics, Data warehousing, Real-time analytics, Big data processing |
Real-time analytics, OLAP, user behavior analytics, clickstream analysis, ad tech, log analytics |
| Scalability | Horizontally scalable with support for distributed computing |
Horizontally scalable, supports distributed architectures for high availability and performance |
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DataBend Overview
DataBend is an open-source, cloud-native data processing and analytics platform designed to provide high-performance, cost-effective, and scalable solutions for big data workloads. The project is driven by a community of developers, researchers, and industry professionals aiming to create a unified data processing platform that combines batch and streaming processing capabilities with advanced analytical features. DataBend’s flexible architecture allows users to build a wide range of applications, from real-time analytics to large-scale data warehousing.
Apache Pinot Overview
Apache Pinot is a real-time distributed OLAP datastore, designed to answer complex analytical queries with low latency. It was initially developed at LinkedIn and later open-sourced in 2015. Pinot is well-suited for handling large-scale data and real-time analytics, providing near-instantaneous responses to complex queries on large datasets. It is used by several large organizations, such as LinkedIn, Microsoft, and Uber.
DataBend for Time Series Data
DataBend’s architecture and processing capabilities make it a suitable choice for working with time series data. Its support for both batch and streaming data processing allows users to ingest, store, and analyze time series data at scale. Additionally, DataBend’s integration with Apache Arrow and its powerful query execution framework enable efficient querying and analytics on time series data, making it a versatile choice for applications that require real-time insights and analytics.
Apache Pinot for Time Series Data
Apache Pinot is a solid choice for working with time series data due to its columnar storage and real-time ingestion capabilities. Pinot’s ability to ingest data from streams like Apache Kafka ensures that time series data can be analyzed as it is being generated, in addition to having options for bulk ingesting data.
DataBend Key Concepts
- DataFusion: DataFusion is a core component of DataBend, providing an extensible query execution framework that supports both SQL and DataFrame-based query APIs.
- Ballista: Ballista is a distributed compute platform within DataBend, built on top of DataFusion, that allows for efficient and scalable execution of large-scale data processing tasks.
- Arrow: DataBend leverages Apache Arrow, an in-memory columnar data format, to enable efficient data exchange between components and optimize query performance.
Apache Pinot Key Concepts
- Segment: A segment is the basic unit of data storage in Pinot. It is a columnar storage format that contains a subset of the table’s data.
- Table: A table in Pinot is a collection of segments.
- Controller: The controller manages the metadata and orchestrates data ingestion, query execution, and cluster management.
- Broker: The broker is responsible for receiving queries, routing them to the appropriate servers, and returning the results to the client.
- Server: The server stores segments and processes queries on those segments.
DataBend Architecture
DataBend is built on a cloud-native, distributed architecture that supports both NoSQL and SQL-like querying capabilities. Its modular design allows users to choose and combine components based on their specific use case and requirements. The core components of DataBend’s architecture include DataFusion, Ballista, and the storage layer. DataFusion is responsible for query execution and optimization, while Ballista enables distributed computing for large-scale data processing tasks. The storage layer in DataBend can be configured to work with various storage backends, such as object storage or distributed file systems.
Apache Pinot Architecture
Pinot is a distributed, columnar datastore that uses a hybrid data model, combining features of both NoSQL and SQL databases. Its architecture consists of three main components: Controller, Broker, and Server. The Controller manages metadata and cluster operations, while Brokers handle query routing and Servers store and process data. Pinot’s columnar storage format enables efficient compression and quick query processing.
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DataBend Features
Unified Batch and Stream Processing
DataBend supports both batch and streaming data processing, enabling users to build a wide range of applications that require real-time or historical data analysis.
Extensible Query Execution
DataBend’s DataFusion component provides a powerful and extensible query execution framework that supports both SQL and DataFrame-based query APIs.
Scalable Distributed Computing
With its Ballista compute platform, DataBend enables efficient and scalable execution of large-scale data processing tasks across a distributed cluster of nodes.
Flexible Storage
DataBend’s architecture allows users to configure the storage layer to work with various storage backends, providing flexibility and adaptability to different use cases.
Apache Pinot Features
Real-time Ingestion
Pinot supports real-time data ingestion from Kafka and other streaming sources, allowing for up-to-date analytics.
Scalability
Pinot’s distributed architecture and partitioning capabilities enable horizontal scaling to handle large datasets and high query loads.
Low-latency Query Processing
Pinot’s columnar storage format and various performance optimizations allow for near-instantaneous responses to complex queries.
DataBend Use Cases
Real-Time Analytics
DataBend’s support for streaming data processing and its powerful query execution framework make it a suitable choice for building real-time analytics applications, such as log analysis, monitoring, and anomaly detection.
Data Warehousing
With its scalable distributed computing capabilities and flexible storage options, DataBend can be used to build large-scale data warehouses that can efficiently store and analyze vast amounts of structured and semi-structured data.
Machine Learning
DataBend’s ability to handle arge-scale data processing and its support for both batch and streaming data make it an excellent choice for machine learning applications. Users can leverage DataBend to preprocess, transform, and analyze data for feature engineering, model training, and evaluation, enabling them to derive valuable insights and build data-driven machine learning models.
Apache Pinot Use Cases
Real-time Analytics
Pinot is designed to support real-time analytics, making it suitable for use cases that require up-to-date insights on large-scale data, such as monitoring and alerting systems, fraud detection, and recommendation engines.
Ad Tech and User Analytics
Apache Pinot is often used in the advertising technology and user analytics space, where low-latency, high-concurrency analytics are crucial for understanding user behavior, optimizing ad campaigns, and personalizing user experiences.
Anomaly Detection and Monitoring
Pinot’s real-time analytics capabilities make it suitable for anomaly detection and monitoring use cases, enabling users to identify unusual patterns or trends in their data and take corrective action as needed.
DataBend Pricing Model
As an open-source project, DataBend is freely available for use without any licensing fees or subscription costs. Users can deploy and manage DataBend on their own infrastructure or opt for cloud-based deployment using popular cloud providers. DataBend itself also provides a managed cloud service with free trial credits available.
Apache Pinot Pricing Model
As an open-source project, Apache Pinot is free to use. However, organizations may incur costs related to hardware, infrastructure, and support when deploying and managing a Pinot cluster. There are no specific pricing options or deployment models tied to Apache Pinot itself.
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