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Fivetran vs Google BigQuery: What are the differences?
## Introduction
Key differences between Fivetran and Google BigQuery:
1. **Data Integration vs. Data Warehouse**: Fivetran is a data integration tool that helps in extracting data from different sources, transforming it, and loading it into a destination of choice, while Google BigQuery is a data warehouse solution that allows for querying and analyzing data stored in it.
2. **Automated Data Pipelines vs. Query Execution**: Fivetran focuses on automating data pipelines by handling data extraction, transformation, and loading processes, making it easier for users to manage their data integration tasks, whereas Google BigQuery specializes in executing SQL queries on large datasets for analytics and reporting purposes.
3. **Pricing Model**: Fivetran typically charges based on the number of data connectors being used and the volume of data being processed, offering a straightforward pricing structure, while Google BigQuery charges based on the amount of data processed during queries and storage usage, providing a pay-as-you-go model for flexibility.
4. **Ease of Use**: Fivetran is known for its user-friendly interface and simple setup process, enabling users to quickly start extracting and syncing data without much technical expertise required, whereas Google BigQuery requires a deeper knowledge of SQL and data modeling to effectively utilize its features.
5. **Managed vs. Self-Managed**: Fivetran provides a fully managed service, handling all aspects of data integration and maintenance, reducing the need for manual interventions, while Google BigQuery is a self-managed platform where users have more control over data storage, partitioning, and optimization.
6. **Scalability**: Google BigQuery is designed to scale horizontally to handle vast amounts of data storage and queries efficiently, making it suitable for large enterprises with big data needs, whereas Fivetran is more focused on simplifying data integration tasks for small to medium-sized businesses.
In Summary, Fivetran and Google BigQuery serve distinct purposes in the data management landscape, with Fivetran specializing in data integration and automation, while Google BigQuery excels in data warehousing and query execution.
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Learn MorePros of Fivetran
Pros of Google BigQuery
Pros of Fivetran
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Pros of Google BigQuery
- High Performance28
- Easy to use25
- Fully managed service22
- Cheap Pricing19
- Process hundreds of GB in seconds16
- Big Data12
- Full table scans in seconds, no indexes needed11
- Always on, no per-hour costs8
- Good combination with fluentd6
- Machine learning4
- Easy to manage1
- Easy to learn0
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Cons of Fivetran
Cons of Google BigQuery
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Cons of Google BigQuery
- You can't unit test changes in BQ data1
- Sdas0
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What is Fivetran?
It helps you centralize data from disparate sources which you can manage directly from your browser. We extract your data and load it into your data destination.
What is Google BigQuery?
Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure.
Load data with ease. Bulk load your data using Google Cloud Storage or stream it in.
Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.
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What companies use Fivetran?
What companies use Google BigQuery?
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What tools integrate with Fivetran?
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What are some alternatives to Fivetran and Google BigQuery?
MySQL
The MySQL software delivers a very fast, multi-threaded, multi-user, and robust SQL (Structured Query Language) database server. MySQL Server is intended for mission-critical, heavy-load production systems as well as for embedding into mass-deployed software.
PostgreSQL
PostgreSQL is an advanced object-relational database management system
that supports an extended subset of the SQL standard, including
transactions, foreign keys, subqueries, triggers, user-defined types
and functions.
MongoDB
MongoDB stores data in JSON-like documents that can vary in structure, offering a dynamic, flexible schema. MongoDB was also designed for high availability and scalability, with built-in replication and auto-sharding.
Redis
Redis is an open source (BSD licensed), in-memory data structure store, used as a database, cache, and message broker. Redis provides data structures such as strings, hashes, lists, sets, sorted sets with range queries, bitmaps, hyperloglogs, geospatial indexes, and streams.
Amazon S3
Amazon Simple Storage Service provides a fully redundant data storage infrastructure for storing and retrieving any amount of data, at any time, from anywhere on the web