Firebolt AWS Redshift Comparison: 6 Critical Differences

• July 26th, 2021

Feature Image Firebolt vs AWS Redshift

Deciding to set up a business that generates lots of data will mean having a good Data Warehouse where data produced can be stored, transformed, and useful insights generated from them to aid operations and general business making decisions. 

There is a host of different analytics parameters that would be considered before choosing a Data Warehouse, these would include the technology used in the architecture of the Data Warehouse, the workload that can be managed at any given time, the performance analytics of the warehouse, scalability, pricing, etc.

In this article, you will learn about Firebolt which is a recent Data Warehouse solution with flexible modern technologies, and AWS Redshift a traditional Data Warehouse built by Amazon to cater to the early needs of Business Intelligence (BI) enterprises, showcasing their strength and highlighting their differences between Firebolt AWS Redshift to enable you to pick one of them when faced with a job at hand as this will be the yardstick of choosing either. 

Table of Contents

Introduction to Firebolt

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Firebolt is an Israeli-based company that regards itself as a modern cloud Data Warehouse of choice for Data Engineering and development teams and promises much more efficient, cheaper analytics of whatever is stored in it.

Firebolt is a Cloud Data Warehouse that handles whatever scale of data you wish to work on accompanied with a high-performance value to deliver production-grade data applications and analytics. It is built for today’s data environments as it leverages your Data Lake and the infinite scale of S3.

Firebolt delivers sub-second and highly concurrent analytics through granular elasticity and seamless scaling of compute nodes as its design is configured to separate storage and compute. Firebolt works with common data file formats such as Parquet, Avro, ORC, etc., therefore, making it readily available for querying to suit your data needs.

Introduction to AWS Redshift

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Amazon Web Services (AWS) is a subsidiary of Amazon saddled with the responsibility of providing a cloud computing platform and APIs to individuals, corporations, and enterprises. AWS offers high computing power, efficient content delivery, database storage with increased flexibility, scalability, reliability, and relatively inexpensive cloud computing services.

Redshift, a part of AWS, is a Cloud-based Data Warehouse service designed by Amazon to handle large data and make it easy to discover new insights from them. Its operations enable you to query and combine exabytes of structured and semi-structured data across various Data Warehouses, Operational Databases, and Data Lakes.

Redshift is built on industry-standard SQL with functionalities to manage large datasets, support high-performance analysis, provide reports, and perform large-scaled database migrations.

Redshift also lets you save queried results to your S3 Data Lake using open formats like Apache Parquet from which additional analysis can be done on your data from other Amazon Web Services such as EMR, Athena, and SageMaker.

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  • Connectors: Hevo supports 100+ integrations to SaaS platforms, files, databases, analytics, and BI tools. It supports various destinations including Google BigQuery, Amazon Redshift, Snowflake, Firebolt Data Warehouses; Amazon S3 Data Lakes; and MySQL, MongoDB, TokuDB, DynamoDB, PostgreSQL databases to name a few.  
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Firebolt AWS Redshift Comparison: Key Differences

To get a complete overview of Firebolt and Redshift, we will carry out an analysis of both Data Warehouses using the following criteria to show their differences: 

1) Firebolt AWS Redshift Comparison: Architecture

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The major difference between various Data Warehouses offering Cloud services is whether the storage and compute operations are separated, this is known as the architecture on which they are built. Under this category, we will look at the differences between Firebolt and Redshift showing their flexibility and the cloud infrastructure they support.

  • Firebolt: Firebolt has a decoupled storage and a compute architecture where it separates its computing process from its storage. Its additional storage and query optimization give room for 10 times better performance and increased efficiency. It supports AWS cloud infrastructure only and allows SQL to be run against external formats to support ingestion. It has multi-tenant and isolated tenancy options for computing and storage and lets you choose an engine node type and any number of nodes for each cluster. 
  • Redshift: Amazon Redshift does not separate its compute and storage operations. Redshift is designed with the shared-nothing Massively Parallel Processing (MPP) architecture. It is made up of Data Warehouse clusters with compute nodes that are split into different units but they all work together. Client applications such as standard JDBC and ODBC drivers can communicate with the architectural system of Redshift and they can be integrated with most existing SQL client applications, BI tools, and Data Mining tools. 

2) Firebolt AWS Redshift Comparison: Scalability

Scalability has to do with the performance level of dedicated resources, handling of continuous data ingestion, and decoupled storage and compute.

  • Firebolt: Firebolt has benefits associated with architectures that separate their compute and storage operations as this improves efficient optimization and allows for the selection of nodes to form a cluster. It also supports write scalability and continuous ingestion of data by users at any time as it has unlimited manual scaling, strong multi-master parallel batch processes with unlimited continuous updates.
  • Redshift: Redshift can only support up to 50 queued queries to be run simultaneously in a cluster and can scale up to 10 clusters automatically in a concurrent query. It also does its scaling both vertically and horizontally and does this automatically providing different clusters access to the same data while being used for different purposes, though it is best suited for batch ingestion and has a limited write throughput as it locks at the table level.

3) Firebolt AWS Redshift Comparison: Performance

This difference has to do with indexing, query optimization performance, ingestion performance/latency, and semi-structured data performance.

  • Firebolt: Firebolt separates its storage and compute therefore improving performance, scalability, and simplifies administration. Firebolt’s indexes for data access, join, and aggregation greatly increases the performance level as performance gains range from 4-6000x across a wide range of queries. This is made possible from Firebolt’s efficient storage F3 format and remote data access where only required data is fetched and not the entire partition. Having the choice to choose the size and number of nodes for each cluster also greatly enhances the performance of Firebolt along with its native semi-structured data support and continuous, low latency ingestion.
  • Redshift: Redshift delivers fast query speeds on large data sets dealing with sizes up to a petabyte and more as it provides a result cache for accelerating repetitive query workload making it efficient for running large amounts of queries. Its ability to perform fast operations emanate from its design architecture of columnar data storage and Massively Parallel Processing because its storage and compute are not separated but carry out operations simultaneously. Redshift can be quite slow when using semi-structured data or low-latency ingestion at any reasonable scale and it does not perform a lot of querying optimization and has no support for indexes. 

4) Firebolt AWS Redshift Comparison: Pricing

This difference talks about computing and storage pricing, compute of instant types, and provision of additional nodes.

  • Firebolt: Firebolt is easy to deploy and resize, add indexing, and change the instance type, therefore, providing on-demand and pre-purchasing pricing plans. Since Firebolt’s storage and compute are separated, you can choose any node size or number of nodes to pay for making it cost-effective and thus it delivers a friendly business plan for most enterprises. Firebolt also supports ad hoc and semi-structured data analytics.
  • Redshift: Redshift offers different pricing options such as on-demand pricing where charges can be set per hour. It has a managed storage system depending on the instance type or the number of self-managed nodes where you can pay for the volume of data monthly making it costly overall for traditional reporting and dashboards. 

5) Firebolt AWS Redshift Comparison: Security

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Security of a Data Warehouse is paramount to any operation to be carried out as this will go a long way in securing a corporation’s data.

  • Firebolt: Firebolt’s architectural security network supports Firewall and WAF, SSL, PrivateLink whitelist/blacklist control, isolated tenancy option, etc.
  • Redshift: Using Redshift, security is shared with AWS as the security of the cloud is taken care of by AWS. Redshift is also compliant with various security standards such as ISO, PCI, HIPAA BAA, and SOC 1, 2, 3. Redshift security network also supports Firewall and WAF, SSL, PrivateLink whitelist/blacklist control, isolated/VPC tenancy, etc.

6) Firebolt AWS Redshift Comparison: Ease of Usage/Data Type

  • Firebolt: Firebolt is simple to use as it is suited for reporting, creating colorful dashboards, interactive and operational purposes. It requires you to have solid SQL and Data Warehouse knowledge, and it supports JSON, XML, Avro, Parquet. 
  • Redshift: Redshift requires you to have the background knowledge of PostgreSQL or similar RDBMS as its query engine is similar to them as it was originally designed to support traditional BI reporting. Redshift also supports JSON.

Conclusion

This article focused on Data Warehousing and touched on the differences that exist between Firebolt and AWS Redshift. It also showed how the two Data Warehouses are structured and stated properties specific to each of them. 

They both handle your data storage in the cloud, therefore, using either of them will be largely determined by the size of your organization and the scale of the job to be executed.

Extracting complex data from a diverse set of data sources can be a challenging task and this is where Hevo Data saves the day! Hevo Data offers a faster way to move data 100+ data sources such as Databases, SaaS applications, CRMs, etc., into your Data Warehouses such as Firebolt or Redshift to be visualized in a BI tool. Hevo Data is fully automated and hence does not require you to code.

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Share your experience of understanding the Firebolt AWS Redshift comparison in the comments section below!

No-code Data Pipeline For Firebolt & Redshift