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  5. Azure Data Factory vs Stratio DataCentric

Azure Data Factory vs Stratio DataCentric

OverviewDecisionsComparisonAlternatives

Overview

Azure Data Factory
Azure Data Factory
Stacks253
Followers484
Votes0
GitHub Stars516
Forks610
Stratio DataCentric
Stratio DataCentric
Stacks5
Followers5
Votes0

Azure Data Factory vs Stratio DataCentric: What are the differences?

Introduction

In the world of data management and integration, Azure Data Factory and Stratio DataCentric are two prominent tools that businesses can use to streamline their data workflows. Understanding the key differences between these two platforms can help organizations make informed decisions about which tool best suits their needs.

  1. Data Integration Capabilities: Azure Data Factory is a cloud-based data integration service that allows users to create, schedule, and manage data pipelines for data migration and transformation. In contrast, Stratio DataCentric is a data platform that offers advanced data integration capabilities, including real-time data ingestion, processing, and analytics. This makes Stratio DataCentric a more comprehensive solution for organizations with complex data integration requirements.

  2. Data Processing and Analytics: Azure Data Factory provides capabilities for data transformation and orchestration, enabling users to build data pipelines for ETL (extract, transform, load) and ELT (extract, load, transform) processes. On the other hand, Stratio DataCentric offers advanced data processing and analytics features, such as machine learning, data visualization, and interactive dashboards, making it suitable for organizations that require in-depth data analysis capabilities.

  3. Scalability and Performance: Azure Data Factory is designed to scale automatically based on workload demands and offers managed services for high availability and performance. Stratio DataCentric, on the other hand, is built to handle large volumes of data and complex analytics tasks, providing scalability and performance optimizations tailored for data-intensive workloads.

  4. Integration with Other Services: Azure Data Factory seamlessly integrates with other Microsoft Azure services, such as Azure SQL Database, Azure Blob Storage, and Azure HDInsight, enabling users to leverage a wide range of cloud-based tools for their data integration workflows. In comparison, Stratio DataCentric offers integration with various open-source and third-party tools, providing flexibility for organizations that use a mix of proprietary and open-source technologies in their data environments.

  5. Security and Compliance: Azure Data Factory provides built-in security features, such as data encryption, role-based access control, and auditing capabilities, to help organizations maintain data security and compliance with industry regulations. Stratio DataCentric also offers robust security measures, including data masking, user authentication, and data governance tools, to ensure data privacy and regulatory compliance in data processing and analytics workflows.

  6. Cost and Licensing: Azure Data Factory follows a pay-as-you-go pricing model, where users pay only for the services and resources they use, making it a cost-effective option for organizations with varying data integration needs. Stratio DataCentric offers flexible licensing options tailored to the specific requirements of organizations, including subscription-based pricing, enterprise licensing, and customized solutions for large-scale data processing and analytics projects.

In Summary, understanding the key differences between Azure Data Factory and Stratio DataCentric can help organizations choose the right data integration and analytics platform based on their unique requirements and use cases.

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Advice on Azure Data Factory, Stratio DataCentric

Vamshi
Vamshi

Data Engineer at Tata Consultancy Services

May 29, 2020

Needs adviceonPySparkPySparkAzure Data FactoryAzure Data FactoryDatabricksDatabricks

I have to collect different data from multiple sources and store them in a single cloud location. Then perform cleaning and transforming using PySpark, and push the end results to other applications like reporting tools, etc. What would be the best solution? I can only think of Azure Data Factory + Databricks. Are there any alternatives to #AWS services + Databricks?

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Comments

Detailed Comparison

Azure Data Factory
Azure Data Factory
Stratio DataCentric
Stratio DataCentric

It is a service designed to allow developers to integrate disparate data sources. It is a platform somewhat like SSIS in the cloud to manage the data you have both on-prem and in the cloud.

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:

Real-Time Integration; Parallel Processing; Data Chunker; Data Masking; Proactive Monitoring; Big Data Processing
Customer-centricity; Omnichannel strategy, Data intelligence
Statistics
GitHub Stars
516
GitHub Stars
-
GitHub Forks
610
GitHub Forks
-
Stacks
253
Stacks
5
Followers
484
Followers
5
Votes
0
Votes
0
Integrations
Octotree
Octotree
Java
Java
.NET
.NET
No integrations available

What are some alternatives to Azure Data Factory, Stratio DataCentric?

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

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.

Apache Kylin

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.

Apache Camel

Apache Camel

An open source Java framework that focuses on making integration easier and more accessible to developers.

Splunk

Splunk

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

Apache Impala

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.

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