Google Cloud Data Fusion vs Stratio DataCentric

Overview

Google Cloud Data Fusion

Stacks25

Followers156

Votes1

Stratio DataCentric

Stacks5

Followers5

Votes0

Google Cloud Data Fusion vs Stratio DataCentric: What are the differences?

Introduction

Google Cloud Data Fusion and Stratio DataCentric are two data integration and transformation tools that enable organizations to efficiently process and analyze data. Despite serving the same purpose, there are several key differences between these two platforms.

1. Pricing Model: Google Cloud Data Fusion has a pay-as-you-go pricing model, where users are charged based on the number and duration of data processing jobs. On the other hand, Stratio DataCentric follows a subscription-based pricing model with fixed fees based on the desired functionality and usage. This difference in pricing models allows organizations to choose the one that aligns best with their budget and usage requirements.

2. User Interface and Ease of Use: Google Cloud Data Fusion provides a visually intuitive and user-friendly interface with a drag-and-drop functionality for data transformation and pipeline creation. In contrast, Stratio DataCentric offers a more complex and technical user interface that requires a certain level of coding knowledge. This difference in user interface design makes Google Cloud Data Fusion more accessible and easier to use for non-technical users.

3. Integration with Data Sources and Sinks: Google Cloud Data Fusion offers a wide range of connectors to diverse data sources and sinks, including relational databases, cloud storage, and BigQuery. Stratio DataCentric, on the other hand, provides limited support for data source and sink integrations, mainly focusing on Kafka and HDFS. This difference in integration capabilities allows Google Cloud Data Fusion to handle a broader range of data sources and sinks compared to Stratio DataCentric.

4. Pre-Built Transformers and Plugins: Google Cloud Data Fusion provides a vast array of pre-built transformers and plugins that enable users to perform various data transformations and integrations. In contrast, Stratio DataCentric offers limited pre-built transformers and plugins, requiring users to develop custom solutions for specific use cases. This difference in the availability of pre-built functionalities makes Google Cloud Data Fusion a more efficient and time-saving option for data integration tasks.

5. Scalability and Performance: Google Cloud Data Fusion is built on top of Google Cloud Platform, leveraging its scalability and high-performance processing capabilities. This allows it to handle large volumes of data and execute complex workflows efficiently. In comparison, Stratio DataCentric may face limitations in terms of scalability and performance as it lacks the cloud infrastructure and resources provided by Google Cloud Platform. This difference in scalability and performance makes Google Cloud Data Fusion a more suitable choice for data processing at scale.

6. Community and Support: Google Cloud Data Fusion benefits from the extensive Google Cloud community and support ecosystem. Users have access to a wide range of documentation, tutorials, and community forums, ensuring prompt assistance and problem-solving. In contrast, Stratio DataCentric has a relatively smaller community and support network, which may limit the availability of resources and assistance for users. This difference in community and support can significantly impact the ease of troubleshooting and getting help when using the respective platforms.

In Summary, Google Cloud Data Fusion offers a pay-as-you-go pricing model, a user-friendly interface, wider integration capabilities, abundant pre-built functionalities, scalability, and extensive community support. Conversely, Stratio DataCentric follows a subscription-based pricing model, a more technical user interface, limited integration options, requires more custom development, may have scalability limitations, and a smaller community and support ecosystem.

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Detailed Comparison

Google Cloud Data Fusion	Stratio DataCentric
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.	It is a unique product that puts your most valuable asset at the core of your business: YOUR DATA. It serves as the backbone for the Digital Transformation of companies. It brings together the latest, most disruptive technologies into a single product that responds to the needs of today’s market:
Code-free self-service; Collaborative data engineering; GCP-native; Enterprise-grade security; Integration metadata and lineage; Seamless operations; Comprehensive integration toolkit; Hybrid enablement	Customer-centricity; Omnichannel strategy, Data intelligence
Statistics
Stacks 25	Stacks 5
Followers 156	Followers 5
Votes 1	Votes 0
Pros & Cons
Pros 1 Lower total cost of pipeline ownership	No community feedback yet
Integrations
Google Cloud Storage Google BigQuery	No integrations available

What are some alternatives to Google Cloud Data Fusion, Stratio DataCentric?

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.

Presto

Distributed SQL Query Engine for Big Data

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.

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.