Snowflake vs Treasure Data

Overview

Treasure Data

Stacks28

Followers44

Votes5

Snowflake

Stacks1.2K

Followers1.2K

Votes27

Snowflake vs Treasure Data: What are the differences?

Storage Architecture: Snowflake and Treasure Data have different storage architectures. Snowflake uses a multi-cluster shared data architecture, where data is stored in multiple virtual warehouses that can be scaled independently. Treasure Data, on the other hand, uses a distributed data storage architecture, which allows for reliable and scalable storage of large volumes of data.
Query Processing: Snowflake and Treasure Data have different approaches to query processing. Snowflake uses a unique two-step query processing model that separates query compilation from query execution. This allows for faster query performance and optimization. Treasure Data, on the other hand, uses a distributed query processing model that allows for parallel execution of queries across multiple nodes, resulting in high query performance.
Data Transformation: Snowflake and Treasure Data have different capabilities for data transformation. Snowflake provides a wide range of built-in functions and operators for data transformation, including support for complex data types and data manipulation language (DML) operations. Treasure Data, on the other hand, provides a rich set of data transformation features, including support for user-defined functions, data aggregation, and data cleansing.
Data Integration: Snowflake and Treasure Data have different approaches to data integration. Snowflake provides native connectors to popular data integration tools, such as Informatica and Talend, allowing for seamless data ingestion and integration. Treasure Data, on the other hand, offers a data collection platform that supports data integration from various sources, including web and mobile devices, IoT devices, and third-party applications.
Security: Snowflake and Treasure Data have different security features. Snowflake provides advanced security features, such as granular access controls, encryption at rest and in transit, and integration with external authentication providers. Treasure Data, on the other hand, offers secure data processing and storage, with features such as data anonymization, access controls, and encryption.
Scalability: Snowflake and Treasure Data have different scalability capabilities. Snowflake is designed to scale horizontally, allowing for seamless scaling of compute and storage resources as data volume and query workload increase. Treasure Data, on the other hand, is built on a distributed architecture that enables horizontal scaling of data processing and storage, allowing for high scalability and performance.

In Summary, Snowflake and Treasure Data differ in their storage architecture, query processing, data transformation capabilities, data integration approaches, security features, and scalability capabilities.

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Advice on Treasure Data, Snowflake

Julien

CTO at Hawk

Sep 19, 2020

Decided

Cloud Data-warehouse is the centerpiece of modern Data platform. The choice of the most suitable solution is therefore fundamental.

Our benchmark was conducted over BigQuery and Snowflake. These solutions seem to match our goals but they have very different approaches.

BigQuery is notably the only 100% serverless cloud data-warehouse, which requires absolutely NO maintenance: no re-clustering, no compression, no index optimization, no storage management, no performance management. Snowflake requires to set up (paid) reclustering processes, to manage the performance allocated to each profile, etc. We can also mention Redshift, which we have eliminated because this technology requires even more ops operation.

BigQuery can therefore be set up with almost zero cost of human resources. Its on-demand pricing is particularly adapted to small workloads. 0 cost when the solution is not used, only pay for the query you're running. But quickly the use of slots (with monthly or per-minute commitment) will drastically reduce the cost of use. We've reduced by 10 the cost of our nightly batches by using flex slots.

Finally, a major advantage of BigQuery is its almost perfect integration with Google Cloud Platform services: Cloud functions, Dataflow, Data Studio, etc.

BigQuery is still evolving very quickly. The next milestone, BigQuery Omni, will allow to run queries over data stored in an external Cloud platform (Amazon S3 for example). It will be a major breakthrough in the history of cloud data-warehouses. Omni will compensate a weakness of BigQuery: transferring data in near real time from S3 to BQ is not easy today. It was even simpler to implement via Snowflake's Snowpipe solution.

We also plan to use the Machine Learning features built into BigQuery to accelerate our deployment of Data-Science-based projects. An opportunity only offered by the BigQuery solution

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Detailed Comparison

Treasure Data	Snowflake
Treasure Data's Big Data as-a-Service cloud platform enables data-driven businesses to focus their precious development resources on their applications, not on mundane, time-consuming integration and operational tasks. The Treasure Data Cloud Data Warehouse service offers an affordable, quick-to-implement and easy-to-use big data option that does not require specialized IT resources, making big data analytics available to the mass market.	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.
Instant Integration- Using td-agent, you can start importing your data from existing log files, web and packaged applications right away.;Streaming or Batch?- You choose! Our data collection tool, td-agent, enables you to stream or batch your data to the cloud in JSON format.;Secure Upload- The connection between td-agent and the cloud is SSL-encrypted, ensuring secure transfer of your data.;Availability- Our best-in-class, multi-tenant architecture uses Amazon S3 to ensure 24x7 availability and automatic replication.;Columnar Database- Our columnar database not only delivers blinding performance, it also compresses data to 5 to 10 percent of its original size.;Schema Free- Unlike traditional databases – even cloud databases – Treasure Data allows you to change your data schema anytime.;SQL-like Query Language- Query your data using our SQL-like language.;BI Tools Connectivity- Treasure Data allows you to use your existing BI/visualization tools (e.g. JasperSoft, Pentaho, Talend, Indicee, Metric Insights) using our JDBC driver.;Enterprise-level Service and Support;No Lock-in- We provide a one-line command to let you export your data anywhere you choose, whenever you choose.	-
Statistics
Stacks 28	Stacks 1.2K
Followers 44	Followers 1.2K
Votes 5	Votes 27
Pros & Cons
Pros 2 Scaleability, less overhead 2 Makes it easy to ingest all data from different inputs 1 Responsive to our business requirements, great support	Pros 7 Public and Private Data Sharing 4 Good Performance 4 User Friendly 4 Multicloud 3 Great Documentation
Integrations
Amazon EC2 G Suite Heroku Engine Yard Cloud Red Hat OpenShift cloudControl	Python Apache Spark Node.js Looker Periscope Mode

What are some alternatives to Treasure Data, Snowflake?

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.

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.

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

Snowflake vs Treasure Data: What are the differences?

Storage Architecture: Snowflake and Treasure Data have different storage architectures. Snowflake uses a multi-cluster shared data architecture, where data is stored in multiple virtual warehouses that can be scaled independently. Treasure Data, on the other hand, uses a distributed data storage architecture, which allows for reliable and scalable storage of large volumes of data.
Query Processing: Snowflake and Treasure Data have different approaches to query processing. Snowflake uses a unique two-step query processing model that separates query compilation from query execution. This allows for faster query performance and optimization. Treasure Data, on the other hand, uses a distributed query processing model that allows for parallel execution of queries across multiple nodes, resulting in high query performance.
Data Transformation: Snowflake and Treasure Data have different capabilities for data transformation. Snowflake provides a wide range of built-in functions and operators for data transformation, including support for complex data types and data manipulation language (DML) operations. Treasure Data, on the other hand, provides a rich set of data transformation features, including support for user-defined functions, data aggregation, and data cleansing.
Data Integration: Snowflake and Treasure Data have different approaches to data integration. Snowflake provides native connectors to popular data integration tools, such as Informatica and Talend, allowing for seamless data ingestion and integration. Treasure Data, on the other hand, offers a data collection platform that supports data integration from various sources, including web and mobile devices, IoT devices, and third-party applications.
Security: Snowflake and Treasure Data have different security features. Snowflake provides advanced security features, such as granular access controls, encryption at rest and in transit, and integration with external authentication providers. Treasure Data, on the other hand, offers secure data processing and storage, with features such as data anonymization, access controls, and encryption.
Scalability: Snowflake and Treasure Data have different scalability capabilities. Snowflake is designed to scale horizontally, allowing for seamless scaling of compute and storage resources as data volume and query workload increase. Treasure Data, on the other hand, is built on a distributed architecture that enables horizontal scaling of data processing and storage, allowing for high scalability and performance.

Snowflake vs Treasure Data

Overview