Choosing 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 SQL Server so you can quickly see how they compare against each other.
The primary purpose of this article is to compare how DataBend and SQL Server 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 SQL Server Breakdown
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.
SQL Server can be deployed on-premises, in virtual machines, or as a managed cloud service (Azure SQL Database) on Microsoft Azure. It is available in multiple editions tailored to different use cases, such as Express, Standard, and Enterprise.
Data analytics, Data warehousing, Real-time analytics, Big data processing
Transaction processing, business intelligence, data warehousing, analytics, web applications, enterprise applications
Horizontally scalable with support for distributed computing
Supports vertical and horizontal scaling, with features like partitioning, sharding, and replication for distributed environments
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.
SQL Server Overview
Microsoft SQL Server is a powerful and widely used relational database management system developed by Microsoft. Initially released in 1989, it has evolved over the years to become one of the most popular database systems for businesses of all sizes. SQL Server is known for its robust performance, security, and ease of use. It supports a variety of platforms, including Windows, Linux, and containers, providing flexibility for different deployment scenarios.
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.
SQL Server for Time Series Data
While Microsoft SQL Server is primarily a relational database, it does offer support for time series data through various features and optimizations. Temporal tables allow for tracking changes in data over time, providing an efficient way to store and query historical data. Indexing and partitioning can be leveraged to optimize time series data storage and retrieval. However, SQL Server may not be the best choice for applications requiring high write or query throughput specifically for time series data, as specialized time series databases offer more optimized solutions as well as a variety of developer productivity features that speed up development time for applications that heavily use time series 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.
SQL Server Key Concepts
- T-SQL: Transact-SQL, an extension of SQL that adds procedural programming elements, such as loops, conditional statements, and error handling, to the standard SQL language.
- SSMS: SQL Server Management Studio, an integrated environment for managing SQL Server instances, databases, and objects.
- Always On: A suite of high availability and disaster recovery features in SQL Server, including Always On Availability Groups and Always On Failover Cluster Instances.
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.
SQL Server Architecture
Microsoft SQL Server is a relational database that uses SQL for querying and manipulating data. It follows a client-server architecture, with the database server hosting the data and processing requests from clients. SQL Server supports both on-premises and cloud-based deployment through Azure SQL Database, a managed service offering in the Microsoft Azure cloud. SQL Server’s architecture includes components such as the Database Engine, which processes data storage and retrieval, and various services for reporting, integration, and analysis.
Free Time-Series Database Guide
Get a comprehensive review of alternatives and critical requirements for selecting yours.
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.
DataBend’s architecture allows users to configure the storage layer to work with various storage backends, providing flexibility and adaptability to different use cases.
SQL Server Features
SQL Server offers advanced security features, such as Transparent Data Encryption, Always Encrypted, and row-level security, to protect sensitive data.
SQL Server supports scaling out through features like replication, distributed partitioned views, and Always On Availability Groups.
SQL Server Integration Services (SSIS) is a powerful platform for building high-performance data integration and transformation solutions.
DataBend Use Cases
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.
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.
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.
SQL Server Use Cases
SQL Server is commonly used as the backend database for enterprise applications, providing a reliable and secure data storage solution.
Data Warehousing and Business Intelligence
SQL Server’s built-in analytical features, such as Analysis Services and Reporting Services, make it suitable for data warehousing and business intelligence applications.
SQL Server’s performance and scalability features enable it to support the demanding workloads of e-commerce platforms, handling high volumes of transactions and user data.
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.
SQL Server Pricing Model
Microsoft SQL Server offers a variety of licensing options, including per-core, server + CAL (Client Access License), and subscription-based models for cloud deployments. Costs depend on factors such as the edition (Standard, Enterprise, or Developer), the number of cores, and the required features. For cloud-based deployments, Azure SQL Database offers a pay-as-you-go model with various service tiers to accommodate different performance and resource requirements.
Get started with InfluxDB for free
InfluxDB Cloud is the fastest way to start storing and analyzing your time series data.