Dremio vs Google Cloud Data Fusion

Overview

Dremio

Stacks116

Followers348

Votes8

Google Cloud Data Fusion

Stacks25

Followers156

Votes1

Dremio vs Google Cloud Data Fusion: What are the differences?

<Write Introduction here>

Architecture: Dremio is a data-as-a-service platform that uses Apache Arrow technology for in-memory analytics, while Google Cloud Data Fusion is a cloud-native data integration service that simplifies ETL processes.
Data Sources Supported: Dremio supports a wide range of data sources including Hadoop, NoSQL databases, and cloud storage systems, while Google Cloud Data Fusion is specifically designed to work with Google Cloud Platform services.
Data Transformation: Dremio allows for interactive and self-service data transformations using SQL and Python, whereas Google Cloud Data Fusion provides a visual interface for building, scheduling, and monitoring data pipelines without the need for coding.
Scalability: Dremio offers high-performance query execution and horizontal scaling capabilities, allowing users to scale their clusters based on workload demands, while Google Cloud Data Fusion is fully managed and automatically scales resources based on the size of the data and processing requirements.
Cost Structure: Dremio follows a subscription-based pricing model with options for both on-premises and cloud deployments, whereas Google Cloud Data Fusion charges users based on the resources consumed per usage.
Security Features: Dremio provides fine-grained access control, data encryption, and audit logging to ensure data security and compliance, while Google Cloud Data Fusion integrates with Google Cloud IAM for authentication and access control management.

In Summary, Dremio and Google Cloud Data Fusion differ in terms of their architecture, supported data sources, data transformation capabilities, scalability, cost structure, and security features.

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Advice on Dremio, Google Cloud Data Fusion

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:

Create pipelines to ingest the data from multiple sources into the data lake
Help me in aggregating and filtering data available in the data lake.
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

Jul 29, 2020

Needs adviceon

Amazon EC2

Tableau

PowerBI

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

Dremio	Google Cloud Data Fusion
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.	A fully managed, cloud-native data integration service that helps users efficiently build and manage ETL/ELT data pipelines. With a graphical interface and a broad open-source library of preconfigured connectors and transformations, and more.
Democratize all your data; Make your data engineers more productive; Accelerate your favorite tools; Self service, for everybody	Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement
Statistics
Stacks 116	Stacks 25
Followers 348	Followers 156
Votes 8	Votes 1
Pros & Cons
Pros 3 Nice GUI to enable more people to work with Data 2 Easier to Deploy 2 Connect NoSQL databases with RDBMS 1 Free Cons 1 Works only on Iceberg structured data	Pros 1 Lower total cost of pipeline ownership
Integrations
Amazon S3 Python Tableau Azure Database for PostgreSQL Qlik Sense PowerBI	Google Cloud Storage Google BigQuery

What are some alternatives to Dremio, Google Cloud Data Fusion?

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

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

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.

Presto

Distributed SQL Query Engine for Big Data

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 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 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

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 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.

Related Comparisons

Dremio vs Google Cloud Data Fusion: What are the differences?

<Write Introduction here>

Architecture: Dremio is a data-as-a-service platform that uses Apache Arrow technology for in-memory analytics, while Google Cloud Data Fusion is a cloud-native data integration service that simplifies ETL processes.
Data Sources Supported: Dremio supports a wide range of data sources including Hadoop, NoSQL databases, and cloud storage systems, while Google Cloud Data Fusion is specifically designed to work with Google Cloud Platform services.
Data Transformation: Dremio allows for interactive and self-service data transformations using SQL and Python, whereas Google Cloud Data Fusion provides a visual interface for building, scheduling, and monitoring data pipelines without the need for coding.
Scalability: Dremio offers high-performance query execution and horizontal scaling capabilities, allowing users to scale their clusters based on workload demands, while Google Cloud Data Fusion is fully managed and automatically scales resources based on the size of the data and processing requirements.
Cost Structure: Dremio follows a subscription-based pricing model with options for both on-premises and cloud deployments, whereas Google Cloud Data Fusion charges users based on the resources consumed per usage.
Security Features: Dremio provides fine-grained access control, data encryption, and audit logging to ensure data security and compliance, while Google Cloud Data Fusion integrates with Google Cloud IAM for authentication and access control management.

In Summary, Dremio and Google Cloud Data Fusion differ in terms of their architecture, supported data sources, data transformation capabilities, scalability, cost structure, and security features.

Dremio vs Google Cloud Data Fusion

Overview