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  5. Google BigQuery vs Heroku Postgres

Google BigQuery vs Heroku Postgres

OverviewDecisionsComparisonAlternatives

Overview

Google BigQuery
Google BigQuery
Stacks1.8K
Followers1.5K
Votes152
Heroku Postgres
Heroku Postgres
Stacks607
Followers314
Votes38

Google BigQuery vs Heroku Postgres: What are the differences?

Introduction: When comparing Google BigQuery and Heroku Postgres, it's crucial to understand the key differences between these two popular data storage and querying solutions.

  1. Scalability: One major difference between Google BigQuery and Heroku Postgres is their scalability. Google BigQuery is a fully managed, serverless data warehouse solution that automatically scales to handle any query load, making it ideal for handling large datasets and complex queries. On the other hand, Heroku Postgres is a traditional relational database that requires manual scaling based on the workload and size of the database.

  2. Cost Structure: Another key difference is in the cost structure of Google BigQuery and Heroku Postgres. Google BigQuery operates on a pay-as-you-go model, where you are charged for the amount of data processed by your queries. In contrast, Heroku Postgres offers a variety of pricing plans based on the storage capacity and features required, making it a more predictable cost option for certain use cases.

  3. Query Performance: Google BigQuery is optimized for running complex analytical queries on large datasets, offering high-speed performance through its distributed computing architecture. Heroku Postgres, while capable of handling complex queries, may not provide the same level of performance for extremely large datasets due to its traditional relational database architecture.

  4. Data Security: Google BigQuery enforces stringent security measures to protect data, including encryption at rest and in transit, fine-grained access controls, and auditing capabilities. Heroku Postgres also offers robust security features, such as SSL encryption and role-based access control, but may require additional configuration and monitoring for compliance with specific security standards.

  5. Ecosystem Integration: Google BigQuery is tightly integrated with other Google Cloud Platform services, allowing for seamless data transfer and analysis across various tools and services. Heroku Postgres, while offering integrations with popular frameworks and tools, may require more manual configuration for integrating with external services outside of the Heroku platform.

  6. Maintenance and Management: Google BigQuery eliminates the need for database administration tasks such as server provisioning, software updates, and performance tuning, as it is a fully managed service. Heroku Postgres, being a self-managed database service, requires more involvement in terms of maintenance, monitoring, and ensuring high availability of the database.

In Summary, Google BigQuery and Heroku Postgres differ in scalability, cost structure, query performance, data security, ecosystem integration, and maintenance and management.

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Advice on Google BigQuery, Heroku Postgres

Jorge
Jorge

Jan 15, 2020

Needs advice

Considering moving part of our PostgreSQL database infrastructure to the cloud, however, not quite sure between AWS, Heroku, Azure and Google cloud. Things to consider: The main reason is for backing up and centralize all our data in the cloud. With that in mind the main elements are: -Pricing for storage. -Small team. -No need for high throughput. -Support for docker swarm and Kubernetes.

51.8k views51.8k
Comments

Detailed Comparison

Google BigQuery
Google BigQuery
Heroku Postgres
Heroku Postgres

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.

Heroku Postgres provides a SQL database-as-a-service that lets you focus on building your application instead of messing around with database management.

All behind the scenes- Your queries can execute asynchronously in the background, and can be polled for status.;Import data with ease- Bulk load your data using Google Cloud Storage or stream it in bursts of up to 1,000 rows per second.;Affordable big data- The first Terabyte of data processed each month is free.;The right interface- Separate interfaces for administration and developers will make sure that you have access to the tools you need.
High Availability;Rollback;Dataclips;Automated Health Checks
Statistics
Stacks
1.8K
Stacks
607
Followers
1.5K
Followers
314
Votes
152
Votes
38
Pros & Cons
Pros
  • 28
    High Performance
  • 25
    Easy to use
  • 22
    Fully managed service
  • 19
    Cheap Pricing
  • 16
    Process hundreds of GB in seconds
Cons
  • 1
    You can't unit test changes in BQ data
  • 0
    Sdas
Pros
  • 29
    Easy to setup
  • 3
    Extremely reliable
  • 3
    Follower databases
  • 3
    Dataclips for sharing queries
Cons
  • 2
    Super expensive
Integrations
Xplenty
Xplenty
Fluentd
Fluentd
Looker
Looker
Chartio
Chartio
Treasure Data
Treasure Data
PostgreSQL
PostgreSQL
Heroku
Heroku

What are some alternatives to Google BigQuery, Heroku Postgres?

Amazon Redshift

Amazon Redshift

It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Amazon EMR

Amazon EMR

It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

Amazon RDS for PostgreSQL

Amazon RDS for PostgreSQL

Amazon RDS manages complex and time-consuming administrative tasks such as PostgreSQL software installation and upgrades, storage management, replication for high availability and back-ups for disaster recovery. With just a few clicks in the AWS Management Console, you can deploy a PostgreSQL database with automatically configured database parameters for optimal performance. Amazon RDS for PostgreSQL database instances can be provisioned with either standard storage or Provisioned IOPS storage. Once provisioned, you can scale from 10GB to 3TB of storage and from 1,000 IOPS to 30,000 IOPS.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

ElephantSQL

ElephantSQL

ElephantSQL hosts PostgreSQL on Amazon EC2 in multiple regions and availability zones. The servers are continuously transferring the Write-Ahead-Log (the transaction log) to S3 for maximum reliability.

Stitch

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Dremio

Dremio

Dremio—the data lake engine, operationalizes your data lake storage and speeds your analytics processes with a high-performance and high-efficiency query engine while also democratizing data access for data scientists and analysts.

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