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  1. Stackups
  2. Application & Data
  3. Databases
  4. Big Data As A Service
  5. Azure Synapse vs etleap

Azure Synapse vs etleap

OverviewComparisonAlternatives

Overview

etleap
etleap
Stacks9
Followers12
Votes0
Azure Synapse
Azure Synapse
Stacks104
Followers230
Votes10

Azure Synapse vs etleap: What are the differences?

## Introduction
This Markdown content will highlight the key differences between Azure Synapse and etleap.

1. **Data Integration Approach**: Azure Synapse offers a comprehensive integrated platform for data integration, analytics, and visualization, while etleap specializes in cloud-based ETL processes, focusing on simplifying data pipeline creation. Azure Synapse provides a more extensive range of data processing capabilities beyond ETL, including data warehousing, big data analytics, and machine learning integration.
   
2. **Scalability and Flexibility**: Azure Synapse is designed for enterprises needing high scalability and performance, with the ability to handle large data volumes efficiently. On the other hand, etleap is more suitable for small to medium-sized businesses looking for a simple and agile ETL solution that can easily scale as their data needs grow.

3. **Cost Structure**: Azure Synapse is a part of Microsoft Azure cloud services, which offer a pay-as-you-go pricing model based on usage and data storage. In contrast, etleap follows a subscription-based pricing structure, making it more predictable for organizations with consistent data processing needs and budgets.

4. **Integration Capabilities**: Azure Synapse seamlessly integrates with other Microsoft services like Power BI, Azure Data Lake, and Azure Machine Learning, providing a unified data platform within the Azure ecosystem. Etleap, while being a cloud-native ETL tool, may have limitations in terms of integrations with other third-party tools or services outside its ecosystem.

5. **Security and Compliance Features**: Azure Synapse offers robust security measures, including built-in data encryption, access controls, and compliance certifications such as HIPAA and GDPR. Etleap also emphasizes data security and compliance but may require additional configurations or third-party tools to achieve the same level of protection as Azure Synapse.

6. **Advanced Analytics Capabilities**: Azure Synapse enables users to perform complex analytics tasks such as real-time data processing, predictive analytics, and AI integration through Azure Machine Learning services. Etleap, while proficient in ETL workflows, may lack the advanced analytics features provided by Azure Synapse.

In Summary, Azure Synapse provides a comprehensive, scalable, and integrated data platform with advanced analytics capabilities, while etleap focuses on simplified ETL processes and cost-effective cloud-based solutions for small to medium-sized businesses.

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Detailed Comparison

etleap
etleap
Azure Synapse
Azure Synapse

Etleap simplifies and automates ETL on AWS. Etleap's data wrangler and modeling tools let users control how data is transformed for analysis, without writing any code, and monitors pipelines to ensure availability and completeness of data.

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

ETL;modeling;SaaS;
Complete T-SQL based analytics – Generally Available; Deeply integrated Apache Spark; Hybrid data integration; Unified user experience
Statistics
Stacks
9
Stacks
104
Followers
12
Followers
230
Votes
0
Votes
10
Pros & Cons
No community feedback yet
Pros
  • 4
    ETL
  • 3
    Security
  • 2
    Serverless
  • 1
    Doesn't support cross database query
Cons
  • 1
    Concurrency
  • 1
    Dictionary Size Limitation - CCI
Integrations
MySQL
MySQL
Amazon Redshift
Amazon Redshift
PostgreSQL
PostgreSQL
No integrations available

What are some alternatives to etleap, Azure Synapse?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

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.

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

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.

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