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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Dremio vs Treasure Data

Dremio vs Treasure Data

OverviewDecisionsComparisonAlternatives

Overview

Treasure Data
Treasure Data
Stacks28
Followers44
Votes5
Dremio
Dremio
Stacks116
Followers348
Votes8

Treasure Data vs Dremio: What are the differences?

Developers describe Treasure Data as "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. On the other hand, Dremio is detailed as "Self-service data for everyone". It is a data-as-a-service platform that empowers users to discover, curate, accelerate, and share any data at any time, regardless of location, volume, or structure. Modern data is managed by a wide range of technologies, including relational databases, NoSQL datastores, file systems, Hadoop, and others.

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

Some of the features offered by Treasure Data are:

  • 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.

On the other hand, Dremio provides the following key features:

  • Democratize all your data
  • Make your data engineers more productive
  • Accelerate your favorite tools

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Advice on Treasure Data, Dremio

karunakaran
karunakaran

Consultant

Jun 26, 2020

Needs advice

I am trying to build a data lake by pulling data from multiple data sources ( custom-built tools, excel files, CSV files, etc) and use the data lake to generate dashboards.

My question is which is the best tool to do the following:

  1. Create pipelines to ingest the data from multiple sources into the data lake
  2. Help me in aggregating and filtering data available in the data lake.
  3. Create new reports by combining different data elements from the data lake.

I need to use only open-source tools for this activity.

I appreciate your valuable inputs and suggestions. Thanks in Advance.

80.5k views80.5k
Comments
datocrats-org
datocrats-org

Jul 29, 2020

Needs adviceonAmazon EC2Amazon EC2TableauTableauPowerBIPowerBI

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.

319k views319k
Comments

Detailed Comparison

Treasure Data
Treasure Data
Dremio
Dremio

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.

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.

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.
Democratize all your data; Make your data engineers more productive; Accelerate your favorite tools; Self service, for everybody
Statistics
Stacks
28
Stacks
116
Followers
44
Followers
348
Votes
5
Votes
8
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
  • 3
    Nice GUI to enable more people to work with Data
  • 2
    Connect NoSQL databases with RDBMS
  • 2
    Easier to Deploy
  • 1
    Free
Cons
  • 1
    Works only on Iceberg structured data
Integrations
Amazon EC2
Amazon EC2
G Suite
G Suite
Heroku
Heroku
Engine Yard Cloud
Engine Yard Cloud
Red Hat OpenShift
Red Hat OpenShift
cloudControl
cloudControl
Amazon S3
Amazon S3
Python
Python
Tableau
Tableau
Azure Database for PostgreSQL
Azure Database for PostgreSQL
Qlik Sense
Qlik Sense
PowerBI
PowerBI

What are some alternatives to Treasure Data, Dremio?

Google BigQuery

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.

Apache Spark

Apache Spark

Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

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 Athena

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.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

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