Amazon Redshift vs Treasure Data

Need advice about which tool to choose?Ask the StackShare community!

Amazon Redshift

1.3K
1.1K
+ 1
103
Treasure Data

22
37
+ 1
5
Add tool

Amazon Redshift vs Treasure Data: What are the differences?

Amazon Redshift: Fast, fully managed, petabyte-scale data warehouse service. Redshift makes it simple and cost-effective to efficiently analyze all your data using your existing business intelligence tools. It is optimized for datasets 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; Treasure Data: Flexible data analytics infrastructure as a service. 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.

Amazon Redshift and Treasure Data belong to "Big Data as a Service" category of the tech stack.

Some of the features offered by Amazon Redshift are:

  • Optimized for Data Warehousing- It uses columnar storage, data compression, and zone maps to reduce the amount of IO needed to perform queries. Redshift has a massively parallel processing (MPP) architecture, parallelizing and distributing SQL operations to take advantage of all available resources.
  • Scalable- With a few clicks of the AWS Management Console or a simple API call, you can easily scale the number of nodes in your data warehouse up or down as your performance or capacity needs change.
  • No Up-Front Costs- You pay only for the resources you provision. You can choose On-Demand pricing with no up-front costs or long-term commitments, or obtain significantly discounted rates with Reserved Instance pricing.

On the other hand, Treasure Data provides the following key features:

  • 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.
Advice on Amazon Redshift and Treasure Data

We need to perform ETL from several databases into a data warehouse or data lake. We want to

  • keep raw and transformed data available to users to draft their own queries efficiently
  • give users the ability to give custom permissions and SSO
  • move between open-source on-premises development and cloud-based production environments

We want to use inexpensive Amazon EC2 instances only on medium-sized data set 16GB to 32GB feeding into Tableau Server or PowerBI for reporting and data analysis purposes.

See more
Replies (3)

You could also use AWS Lambda and use Cloudwatch event schedule if you know when the function should be triggered. The benefit is that you could use any language and use the respective database client.

But if you orchestrate ETLs then it makes sense to use Apache Airflow. This requires Python knowledge.

See more
Recommends
Airflow

Though we have always built something custom, Apache airflow (https://airflow.apache.org/) stood out as a key contender/alternative when it comes to open sources. On the commercial offering, Amazon Redshift combined with Amazon Kinesis (for complex manipulations) is great for BI, though Redshift as such is expensive.

See more
Recommends

You may want to look into a Data Virtualization product called Conduit. It connects to disparate data sources in AWS, on prem, Azure, GCP, and exposes them as a single unified Spark SQL view to PowerBI (direct query) or Tableau. Allows auto query and caching policies to enhance query speeds and experience. Has a GPU query engine and optimized Spark for fallback. Can be deployed on your AWS VM or on prem, scales up and out. Sounds like the ideal solution to your needs.

See more
Decisions about Amazon Redshift and Treasure Data
Julien Lafont

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

See more
Get Advice from developers at your company using Private StackShare. Sign up for Private StackShare.
Learn More
Pros of Amazon Redshift
Pros of Treasure Data
  • 37
    Data Warehousing
  • 27
    Scalable
  • 16
    SQL
  • 14
    Backed by Amazon
  • 5
    Encryption
  • 1
    Cheap and reliable
  • 1
    Isolation
  • 1
    Best Cloud DW Performance
  • 1
    Fast columnar storage
  • 2
    Scaleability, less overhead
  • 2
    Makes it easy to ingest all data from different inputs
  • 1
    Responsive to our business requirements, great support

Sign up to add or upvote prosMake informed product decisions

Sign up to add or upvote consMake informed product decisions

What is 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.

What is Treasure Data?

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.

Need advice about which tool to choose?Ask the StackShare community!

Jobs that mention Amazon Redshift and Treasure Data as a desired skillset
What companies use Amazon Redshift?
What companies use Treasure Data?
See which teams inside your own company are using Amazon Redshift or Treasure Data.
Sign up for Private StackShareLearn More

Sign up to get full access to all the companiesMake informed product decisions

What tools integrate with Amazon Redshift?
What tools integrate with Treasure Data?

Sign up to get full access to all the tool integrationsMake informed product decisions

Blog Posts

Jul 9 2019 at 7:22PM

Blue Medora

+8
11
1782
+42
52
19829
+44
109
49943
What are some alternatives to Amazon Redshift and Treasure Data?
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 Athena
Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.
Amazon DynamoDB
With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
Amazon Redshift Spectrum
With Redshift Spectrum, you can extend the analytic power of Amazon Redshift beyond data stored on local disks in your data warehouse to query vast amounts of unstructured data in your Amazon S3 “data lake” -- without having to load or transform any data.
Hadoop
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
See all alternatives