Find the best-fit ETL
5X recommends the right ETL based on your needs. Saving you the hassle of evaluating multiple vendors & negotiating with their sales teams
How it works:
Tell us about your challenges, use cases & goals
5X data expert shares personalized recommendations
We provision the ETL vendor & integrate it with your data platform in a matter of hours
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“5X has excellent recommendations for the best-in-class vendors for each layer in the data stack. And with sound reasoning.”
Anthony M. Jerkovic
What type of data do I need to store?
5X Tip: Think about the mix of data types you're dealing with—structured, semi-structured, relational, or non-relational. Make sure the chosen solution is a good fit for your specific data formats, even handling mixed formats like JSON or unstructured text.
What other data integrations do I need?
5X Tip: Your data warehouse is the heart of your data platform, and it integrate with the other tools you're using—whether it's for ingesting data, BI, or anything else. If you're bringing in data directly, having connectors is a must for smooth operations.
How do I weigh the benefits of batch processing against real-time processing?
5X Tip: It depends on your needs. Batch processing is great for handling large volumes of data at once, while real-time processing provides immediate insights. Consider the nature of your data and the speed at which you need results.
How do I assess security and compliance?
5X Tip: Choose a data warehouse with built-in encryption, authentication, and authorization features to keep your sensitive data safe and sound. Make sure it plays nice with industry regulations like GDPR or HIPAA that your organization follows. Also, check if the provider has security certifications and features like audit trails and monitoring.
Should I go for a vendor that supports massively parallel processing (MPP)?
5X Tip: Opt for a data warehouse that supports MPP, which can seriously boost query execution speed. Look for a platform that can distribute queries simultaneously across multiple nodes or clusters.
Massively Parallel Processing (MPP) is a computing architecture where multiple processors work together to handle large datasets and execute tasks concurrently.
How’s the performance of the data warehouse?
5X Tip: The performance of a data warehouse is important for ensuring that you can access your data quickly and easily. You should consider the volume of data you need to store, the frequency of queries, and the response time requirements. Assess how well the data warehouse handles queries of varying complexity and data sizes.
What’s my budget and the cost structure of the data warehouse?
5X Tip: Data warehouse pricing hinges on storage, compute resources, and features. Select a data warehouse that aligns with your budget and growth plans. To budget wisely:
- assess your storage and processing needs,
- prioritize scalability, and
- balance setup costs with long-term operational expenses.
Comparison Matrix
Founded at
Headquarter
Cloud Compatibility
Pricing Plans
Pros
Cons
Pricing
Structure
Compute
cost
Scalability
Ease of use
Real-time data processing
Partner ecosystem
Customer support
User access control
Performance
Cost management
Other features
Snowflake
No upfront costs, only pay for queries run on data stored in S3, can be cost-effective, especially for companies dealing with massive datasets and infrequent data analysis needs.
- Medium (0.5x): $8.00
- Large (x): $16.00
- XLarge (2x):$32.00
Separate storage and compute allows you to scale independently of each other
User-friendly SQL interface, little to no learning curve for existing Redshift users. Great for users with or without any coding experience.
Snowflake combined with Apache Kafka can provide cost-efficient near real-time analytics, but requires additional setup
Widest range of data integration partners, and tier 1 database integration for all SaaS tools
Offers community support, a robust knowledge base, diverse support plans, and comprehensive training; yet, standard users may face slow response time depending on the issue.
Discretionary Access Control (DAC) and
Role-Based Access Control (RBAC)
Low latency due to its optimized architecture for data storage and processing.
More hassle-free in most of the scenarios with the automatic partitioning & clustering, column compression, and multi-cluster auto-scaling, preventing spikes in cost due to sub-optimal setup.
It is cheaper for development and testing since you only get charged based on the compute hours.
- Snowpark: Run spark queries
- Snowflake Time Travel: Historical data access
- Object-level access control
- Snowflake Fail-safe: Historical data is protected in the event of a system failure or other event
- Snowsight for account and general management
- SnowSQL (Python-based command line client)
- Snowpipe to load data in micro-batches from internal and external stages.
Google BigQuery
Based on storage and compute consumption. It offers five plans: Free, Storage, Compute, Data ingestion, Data extraction.
- 300 slots (0.5x): $8.22
- 600 slots (1x): $16.44
- 1200 slots (2x): $32.88
Serverless and fully managed architecture allows for seamless scalability, with no resources to provision or manage
GBQ is compatible with GCP services and Google Workspace suite. Intuitive SQL-like interface further simplifies query writing and execution, enhancing overall user experience.
Low-latency replication from relational databases directly to BigQuery for near real-time insights.
Offers almost identical integrations as Snowflake. Integrations with other Google Cloud Platform services, such as Cloud Storage, Cloud Functions, Google Workspace suite, and Data Studio are a plus.
Robust customer support through extensive self-serve options like tutorials, documentation, and community engagement. For personalized assistance, you can choose from the varying support plans.
Granular IAM-based permissions with basic, predefined, and custom roles.
Low latency queries due to its fully managed server when used for high speed analysis in large datasets. Also depends on data size and structure, query complexity, and slot allocation.
Offers flexibility to choose between on-demand and flat-rate pricing.
User-level cost management is a challenge.
Project-level monitoring is available but you can’t tell who is running useful or bad queries. Admin rules to control query execution could bring down costs.
- Duet AI: Natural language chat assistance for real-time guidance on performing specific tasks
- Looker Studio: Built-in BI to create and share insights
- BigQuery geospatial: Enhance your analytics workflows with location intelligence
- Data clean rooms: Create a low-trust collaborative environment without copying or moving the underlying data right within BigQuery.
Amazon Redshift
Pay for storage, compute, and data processed; often high due to advanced processing capabilities. Priced based on DBU, usually 2-5x more expensive than EMR cluster from AWS.
- 3x ra3.4xlarge (0.5x): $9.78
- 5x ra3.4xlarge (1x): $16.30
- 10x ra3.4xlarge (2x): $32.60
Limited within the AWS ecosystem
User-friendly SQL interface, suitable for traditional data engineers.
Can integrate with Kinesis for streaming data but primarily processes data in batch mode in Redshift.
Redshift’s partner ecosystem isn’t as vast as its counterparts. Heavily depending on AWS ecosystem. Note: Some integrations are only available in select AWS Regions.
Offers a ticket-based support system where specialists connect to resolve AWS services issues.
Column Level Access Control and Role-Based Access Control (RBAC)
Higher latency, especially for complex and large-scale processing tasks due to its reliance on S3 for data storage.
Most cost-efficient with the right set up of clusters and proper SQL transformations and queries. Require skilled engineers.
For example, the cost is based on the data scanned and if there is no partitioning column provided on tables, then AWS will scan the entire table and charge $5 per TB of data.
Redshift performance is susceptible to cache misses in the shared query compilation cache.
- Massively Parallel Processing
- Column-oriented data store
- Result caching to deliver sub-second response times for repeat queries
- Automated infrastructure provisioning
- Fault tolerance using data re-replication and node replacement
- Network isolation to restrict network access to organization's cluster using Amazon VPC
Snowflake
Snowflake is a fully-managed cloud-native data platform providing scalable data warehousing, data engineering, and data security solutions.
Founded at
Headquarter
Cloud Compatibility
Pricing Plans
Based on actual storage, compute, and cloud usage
Note: A Snowflake credit is a unit of measure consumed when using resources such as a virtual warehouse, cloud services layer, or serverless features.
Our Recommendation
Use Snowflake if ...
- You need to run unlimited concurrent workloads within seconds
- You prefer scalability features like auto-scaling and auto-suspend
- You’re looking for a fully SQL-based approach
Pros
Large vendor ecosystem that has native integration with more vendors, such as BI/reverse ETL/data observability tools, and other SaaS tools such as ads channels, event tracking tools, and payments.
Auto-scaling capabilities for compute resources offer ease of use in handling varying workloads.
Provides row-level & column-level data governance.
Auto-suspension and auto-resumption features simplify & automate warehouse monitoring & usage based on the workload.
Snowflake has a Partner Connect program where you can choose from different third party tools to integrate, selecting the one that works best for your business.
Cons
The fully-managed nature of Snowflake means users have less customization over the underlying infrastructure.
Need a workaround solution for Google connectors / sources compared to BigQuery.
The Snowflake web interface allows to create and manage Snowflake objects like virtual warehouses and databases, load limited data into tables, execute ad hoc queries and other DDL/DML commands, and view past queries.
User Interface
Intuitive navigation simplifies user experience with easy-to-use menus and navigation.
Clean design with minimum unnecessary elements for a focused and clutter-free workspace.
SQL Editor
You can format SQL queries in Snowsight Worksheets.
Standard SQL support enables users to write queries using standard SQL syntax.
Syntax highlighting of code elements for improved readability.
Auto-complete speeds up query writing by suggesting and completing statements.
Real-time error detection identifies errors as users type, aiding in quick debugging.
Transparent data exploration, aligning with a culture of informed decision-making
Discretionary Access Control (DAC): Every digital entity is assigned an owner. The owner can decide who else is allowed to access or interact with the object.
Role-Based Access Control (RBAC): Permissions are grouped into roles and users are assigned these roles based on their access levels.
Snowflake’s cost management framework is divided into three components:
Visibility: Helps you understand cost sources, get to the core of it, attribute them to the right entities within your org, and monitor them to avoid unnecessary spends. The admin view provides an expensive query view, top warehouses by cost, and cost predictions. Additionally, it offers a very dynamic view of credit usage trends that are customizable. Users can set pulses to track resource usage.
Controls: Kind of extends the purpose of Visibility一control costs by setting limits to, say, how long a query can.
Optimization: Snowflake recommends taking action whenever your Snowflake usage follows any pattern like blocked queries due to transaction locks, copy commands with poor selectivity, single row inserts and fragmented schemas, etc. Check out the recommendations for different usage patterns here.
Pricing
Structure
Compute
cost
Scalability
Ease of use
Real-time data processing
Partner ecosystem
Customer support
User access control
Performance
Cost management
Other features
Google BigQuery
Google BigQuery (GBQ) is a fully managed, serverless cloud data warehouse by Google Cloud Platform (GCP). It is designed for analyzing large datasets in real-time using SQL-like queries.
Founded at
Headquarter
Cloud Compatibility
Pricing Plans
Based on storage and compute consumption
Our Recommendation
Use BigQuery if ...
- You need a highly scalable solution to analyze very large datasets, of the order of petabytes.
- Require quick and responsive query processing for complex analytical tasks.
- Your organization already uses other Google Cloud services or products.
Pros
Advanced query optimization techniques like parallel processing, table partitioning, and columnar storage ensure optimal query performance.
Can handle really large datasets and scale to PB-sized warehouses.
As GBQ is fully-managed, maintenance and infrastructure will be the least of your worries.
Cons
Managing costs associated with large query volumes and data storage in BigQuery can be intricate. It needs vigilant monitoring and optimization.
Since GBQ is a part of GCP, you have to completely rely on GCP for all your data warehousing needs.
While it has integrations within the GCP suite, GBQ offers limited non-GCP integrations compared to Snowflake. Plus, it’s not compatible with non-GCP data warehouses like AWS and Azure.
User Interface
Clean interface that even non-technical users can easily get started. The SQL-like interface makes it easy to write and run queries. Users can retrieve the data they need using simple SQL queries, without having to be a pro at coding.
Intuitive and well-organized service navigation menus, making it easy to locate and access essential features with ease.
SQL Editor
Supports standard SQL syntax, making it accessible for regular SQL users.
The SQL editor uses syntax highlighting for enhanced code readability, making it easy to write and understand queries.
GBQ excels in fast query processing even for complex queries.
Granular permissions: Fine-grained control over access permissions allows admins to specify access at a detailed level.
IAM-based access control: Integration with Identity and Access Management (IAM) ensures secure access control within the broader Google Cloud ecosystem.
Detailed cost breakdown: GBQ offers transparency in its cost structure with a detailed breakdown of data processing costs.
Query cost controls: Users can implement query cost controls by setting quotas and limits to manage and predict spending
Pricing
Structure
Compute
cost
Scalability
Ease of use
Real-time data processing
Partner ecosystem
Customer support
User access control
Performance
Cost management
Other features
Redshift
Amazon Redshift is a fully managed, SQL-based, petabyte-scale cloud data warehouse solution provided by AWS Services.
Founded at
Headquarter
Cloud Compatibility
Pricing Plans
Node-based pricing with options for On-Demand or Reserved models
Our Recommendation
Use Redshift if ...
- You heavily rely on AWS services as a part of your operations.
- Complex analytical queries and large datasets are integral to your business.
- You want to access and analyze data without all of the configurations of a provisioned data warehouse.
- You need a zero-ETL approach to unify data across databases, data lakes, and data warehouses.
Pros
Linear Scaling from GBs to PBs
Familiar SQL language simplifies adoption
Built-in machine learning capabilities
Self-learning and self-tuning capabilities for performance optimization
Integration with Apache Spark to analyze large datasets
High concurrency support
Cons
Requires some manual intervention for certain configurations
Handling high concurrency may lead to performance issues during simultaneous query executions
Deploying Amazon Redshift on non-AWS servers may pose compatibility issues
No real distinction between storage and compute
While Redshift integrates well with other AWS services, ingesting data from external sources can be tedious.
Query performance can be slower than other warehousing solutions
User Interface
Redshift's straightforward SQL interface simplifies data warehouse management, making it accessible to data analysts and SQL developers.
Teams familiar with PostgreSQL can seamlessly transition to Redshift's query engine, as they share the same interface.
Some users complain if the UI could be more developer-friendly.
SQL Editor
Web-based analyst workbench to share, explore, and collaborate on data with teams using SQL in a common notebook interface.
Amazon Q generative SQL allows users to write queries in plain English directly within the query editor and create SQL code recommendations. Note that these features are subject to data access permissions.
Use Query Editor's navigator and visual wizards to browse database objects, create tables, and functions.
Collaborate and share query versions, results, and charts effortlessly with automatic version management in the query editor.
Amazon Redshift provides service-specific resources, actions, and condition context keys for IAM permission policies.
The account admin can attach permission policies to IAM identities (users, groups, roles) and services like AWS Lambda.
The admin gets to decide who gets the permissions, the resources they get access to, and the specific actions to allow on those resources.
Learn more about IAM access control here.
Adjust nodes based on actual usage.
Downsize during low demand for cost savings.
Purchase Reserved Instances (RI) for predictable workloads.
Optimize COPY command for smart data loading.
Implement efficient compression and distribution strategies.
Pricing
Structure
Compute
cost
Scalability
Ease of use
Real-time data processing
Partner ecosystem
Customer support
User access control
Performance
Cost management
Other features
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