Google BigQuery vs Panoply

Overview

Google BigQuery

Stacks1.8K

Followers1.5K

Votes152

Panoply

Stacks9

Followers17

Votes0

Google BigQuery vs Panoply: What are the differences?

<Write Introduction here>

Scalability: Google BigQuery is highly scalable, allowing users to process and analyze massive datasets quickly and efficiently. It can handle petabytes of data in a matter of seconds, making it ideal for organizations with large data requirements. In comparison, Panoply's scalability is limited and may not be optimal for handling extremely large datasets at the same speed as Google BigQuery.
Storage Costs: Google BigQuery charges users based on the amount of data processed, making it cost-effective for organizations with sporadic or fluctuating data usage patterns. On the other hand, Panoply charges users based on the volume of data stored, which can lead to higher costs for organizations with constantly growing data volumes.
Query Performance: Google BigQuery uses a distributed architecture to execute queries in parallel, resulting in high query performance and minimal latency. Panoply, while efficient in query processing, may not match the performance levels of Google BigQuery due to differences in underlying technologies and infrastructure.
Ease of Use: Google BigQuery offers a user-friendly interface with SQL-like queries, making it easier for analysts and data scientists to work with data. Panoply, although user-friendly, may require more advanced knowledge and expertise to fully utilize its capabilities, especially when it comes to complex data processing tasks.
Data Integration: Google BigQuery supports seamless integration with other Google Cloud Platform services and popular data visualization tools, enabling users to streamline their data workflows. Panoply also offers data integration capabilities but may not have the same level of integration options and flexibility as Google BigQuery.
Real-time Data Processing: Google BigQuery supports real-time data processing through Dataflow and streaming inserts, enabling users to analyze data as it flows into the system. Panoply, while capable of near real-time processing, may have limitations in handling high-velocity data streams and providing real-time insights to users.

In Summary, Google BigQuery offers superior scalability, query performance, and integration options compared to Panoply, while Panoply may provide cost advantages and ease of use for organizations with smaller data requirements.

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Detailed Comparison

Google BigQuery	Panoply
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.	It is the data warehouse built for analysts. Our data management platform automates all three key aspects of the data stack: data collection, management, and query optimization.
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.	Data warehouse; Business Intelligence;Optimized Query Engine
Statistics
Stacks 1.8K	Stacks 9
Followers 1.5K	Followers 17
Votes 152	Votes 0
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	No community feedback yet
Integrations
Xplenty Fluentd Looker Chartio Treasure Data	HubSpot MySQL Metabase Google Analytics Airbrake Braintree Amazon S3 QuickBooks Tableau PostgreSQL

What are some alternatives to Google BigQuery, Panoply?

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

Amazon EMR

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

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 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.

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

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—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.

Cloudera Enterprise

Cloudera Enterprise includes CDH, the world’s most popular open source Hadoop-based platform, as well as advanced system management and data management tools plus dedicated support and community advocacy from our world-class team of Hadoop developers and experts.

Airbyte

It is an open-source data integration platform that syncs data from applications, APIs & databases to data warehouses lakes & DBs.

Related Comparisons

Google BigQuery vs Panoply: What are the differences?

<Write Introduction here>

Scalability: Google BigQuery is highly scalable, allowing users to process and analyze massive datasets quickly and efficiently. It can handle petabytes of data in a matter of seconds, making it ideal for organizations with large data requirements. In comparison, Panoply's scalability is limited and may not be optimal for handling extremely large datasets at the same speed as Google BigQuery.
Storage Costs: Google BigQuery charges users based on the amount of data processed, making it cost-effective for organizations with sporadic or fluctuating data usage patterns. On the other hand, Panoply charges users based on the volume of data stored, which can lead to higher costs for organizations with constantly growing data volumes.
Query Performance: Google BigQuery uses a distributed architecture to execute queries in parallel, resulting in high query performance and minimal latency. Panoply, while efficient in query processing, may not match the performance levels of Google BigQuery due to differences in underlying technologies and infrastructure.
Ease of Use: Google BigQuery offers a user-friendly interface with SQL-like queries, making it easier for analysts and data scientists to work with data. Panoply, although user-friendly, may require more advanced knowledge and expertise to fully utilize its capabilities, especially when it comes to complex data processing tasks.
Data Integration: Google BigQuery supports seamless integration with other Google Cloud Platform services and popular data visualization tools, enabling users to streamline their data workflows. Panoply also offers data integration capabilities but may not have the same level of integration options and flexibility as Google BigQuery.
Real-time Data Processing: Google BigQuery supports real-time data processing through Dataflow and streaming inserts, enabling users to analyze data as it flows into the system. Panoply, while capable of near real-time processing, may have limitations in handling high-velocity data streams and providing real-time insights to users.

Google BigQuery vs Panoply

Overview