Azure HDInsight vs etleap

Overview

Azure HDInsight

Stacks29

Followers138

Votes0

etleap

Stacks9

Followers12

Votes0

etleap vs Azure HDInsight: What are the differences?

Developers describe etleap as "user-friendly, sophisticated ETL-as-a-service on AWS". 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. On the other hand, Azure HDInsight is detailed as "A cloud-based service from Microsoft for big data analytics". It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.

etleap and Azure HDInsight are primarily classified as "Big Data as a Service" and "Big Data" tools respectively.

Some of the features offered by etleap are:

ETL
modeling
SaaS

On the other hand, Azure HDInsight provides the following key features:

Fully managed
Full-spectrum
Open-source analytics service in the cloud for enterprises

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Detailed Comparison

Azure HDInsight	etleap
It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.	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.
Fully managed; Full-spectrum; Open-source analytics service in the cloud for enterprises	ETL;modeling;SaaS;
Statistics
Stacks 29	Stacks 9
Followers 138	Followers 12
Votes 0	Votes 0
Integrations
IntelliJ IDEA Apache Spark Kafka Visual Studio Code Hadoop Apache Storm HBase Apache Hive Azure Data Factory Azure Active Directory	MySQL Amazon Redshift PostgreSQL

What are some alternatives to Azure HDInsight, etleap?

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.