Big Data Processing Services Comparison: Alibaba Cloud, AWS, Google Cloud, IBM, Microsoft

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big data processing

The amount of data that we generate every day is unbelievable. Businesses around the world generate over 2.5 quintillion bytes of data per day. A quintillion is a billion times a billion. That’s huge by anyone’s standards.

So, how is it possible for business leaders to efficiently gain insights into data?

This is where big data processing comes into play. It lets you to, firstly, gather, integrate, and analyze your data all at once – structured, semi-structured, and unstructured – irrespective of type, size, format, or source. Secondly, it enables you to quickly scale to large volumes of data and analyze them for insights.

It also helps in reducing costs and making fast decisions. Cloud-based big data analytics helps businesses analyze the data instantly, so decision making is much faster.

Top big data service providers

  • Alibaba Cloud
  • Amazon Web Services (AWS)
  • Google Cloud
  • IBM
  • Microsoft

Part of choosing the best big data processing tools for your company is making sure that the tool aligns with your business’ objective. There are various big data processing tools available in the market that focus on a specific use case. But, just because one tool works for a company does not necessarily mean it will work for another.

So, before going ahead, do identify the services of those providers that match the environment of your organization.

Let’s see a few details about these services.

#1 Alibaba Cloud

Alibaba Cloud has been one of the fastest-growing cloud computing platforms in the world. It comprises a variety of products and services to allow quick and effective big data development and intelligent analysis. The computing infrastructure supports a variety of cloud computing capabilities such as machine learning, data lake analytics, etc.

big data processing

Alibaba Cloud’s Big Data processing architecture. Source: Alibaba Cloud

Several big processing and analysis tools are provided by Alibaba Cloud to address different business needs.

  • E-MapReduce: Elastic MapReduce aids the processing and analysis of huge amounts of data. Based on open-source Apache Hadoop and Apache Spark, it manages your data in various scenarios like data warehousing, trend analysis, and online and offline data processing.
  • Realtime Compute: Realtime Compute offers a one-stop, high-performance platform that enables real-time big data processing based on Apache Flink. It is widely used in diverse scenarios, such as streaming data processing, offline data processing, and data lake computing. With Realtime Compute, you can process and analyze big data in real time for business insights and decision making.
  • MaxCompute: The MaxCompute service is mainly used for large scale data warehousing. It uses a wide range of importing solutions and distributed models to let users reduce production costs and prevent data breaches.
  • DataWorks: This service will secure your offline data development environment. It also provides offline job scheduling, data permission management, and other great features.
  • Quick BI: It uses drag and drop features which allow you to perform data analytics, data exploration, and make data-driven decisions.
  • DataV: This service uses geographic information systems to show multidimensional data at a rapid speed. This helps users to understand the patterns that are integrated into a single user-friendly interface.

Alibaba Cloud big data services offer flexible payment options like cluster payment type, subscription based or Pay-As-You-Go while using MaxCompute.

Visit website: Alibaba Cloud

Also Read: Streaming Data Services Comparison: Alibaba Cloud, AWS, Azure, Google Cloud, IBM Cloud

#2 Amazon Web Services (AWS)

AWS provides a wide range of big data processing tools. With these, you can do all from data processing in real-time to machine learning implementation for applications. AWS offers four main products for cloud-based big data analytics: Elastic MapReduce (EMR), Kinesis, Redshift, and Machine Learning (ML).

big data processing

AWS’s big data analytics diagram. Source: AWS


  • Elastic MapReduce (EMR): It provides a variety of Hadoop-related tools that let you process massive amounts of data. You can also transform and move your data into and out of other AWS databases using EMR.
  • Kinesis: Amazon Kinesis is a fully managed service processing streaming high frequency of data on real-time basis. You can push data in real-time to Amazon Kinesis stream where the data is processed by consuming applications through Kinesis Client Library and Connector Library.
  • Redshift: Amazon Redshift lets you acquire new insights from your data for your business and customers. It gives fast query and I/O performance for any size dataset through columnar storage technology with the likelihood to parallelize and distribute queries across multiple nodes.
  • Machine Learning: It helps you perform predictive analytics without the need to learn complex algorithms. To do so, users are guided to select and prepare data, training, and evaluating predictive models using a simple wizard-based UI.

For Amazon Kinesis and ML, the charges are based on Pay-As-You-Go; for EMR, you pay for the hours the cluster is up; and for Redshift, the cost is based on the size and number of nodes of your cluster.

Visit website: AWS

Also Read: IoT security comparison: Alibaba Cloud, AWS, Azure, Google Cloud, IBM Cloud

#3 Google Cloud

Google Cloud provides various services for big data processing. It offers integrated and end to end solutions for Big Data that helps you to gather, process, analyze, and transfer data on a single platform. It provides a cloud-based analytics platform like BigQuery to scan data in very little time, terabytes in seconds, and petabytes in minutes.

big data processing

Google Cloud’s complete big data processing lifecycle. Source: Google Cloud Platform

Here are Google Cloud’s solutions for big data processing:

  • Cloud Pub/Sub: Pub/Sub is a message queue broker using which applications can exchange messages quickly, reliably, and asynchronously.
  • Cloud Dataprep: This tool is mainly used for visualizing, exploring, and preparing data that you work with. This simplifies building of ETL pipelines and automates the data engineer’s job.
  • Cloud DataFlow: This is a unified programming model that aids in data processing patterns including ETL, batch and stream processing.
  • Cloud Dataproc: Dataproc is a managed Apache Hadoop and Apache Spark service with pre-installed open source data tools for batch processing, querying, streaming, and machine learning. Dataproc automation helps you create clusters quickly, manage them easily, and save money by turning clusters off when you don’t need them. With less time and money spent on administration, you can focus on your jobs and your data.
  • BigQuery: It is a data warehouse that lets you store and query massive datasets, i.e. up to hundreds of petabytes. You can also use it in interactive queuing and offline data analytics.

You can choose from two payment models; one you pay for each processed Terabyte or on monthly basis. Also, you pay separately for the information stored (per GB) and executed queries.

Visit website: Google Cloud

Also Read: 5 Best DevOps Automation tools comparison in 2020

#4 IBM

IBM is one of the best vendors for Big Data-related solutions. IBM Big Data processing services provide features such as data storage, data management, and data analysis.

The IBM Analytics Engine is mainly suited for situations when you need to analyze data from a wide range of sources. This solution is developed on separate integration points and compute and storage infrastructure, including Hortonworks Data Platform and Apache Spark.

IBM’s data and analytics reference architecture. Source: IBM

Here are the set of solutions that IBM Analytics Engine provides for big data processing:

  • IBM Data Refinery: Data Refinery is used to shape large amounts of unstructured data into consumable and quality information. This helps you to visualize your data by automated data type detection and business classifications.
  • IBM Cloud Object Storage: Object Storage is primarily used for storing and accessing unstructured data. It can store data from a myriad of sources in a simple and cost-effective way. It can be commonly used for: data archiving and backup, mobile and web apps, scalable, persistent storage for analytics.
  • IBM Data Catalog: The data stored in Object Storage can only be accessed via Data Catalog. Data Catalog helps in the discovery of data quickly, access, curate, categorize, and share data and their relationships with the members of the organization.
  • IBM Data Science Experience (DSX), now Watson Studio: Watson Studio tool is mainly for data scientists, which helps to prepare data and develop models on a large scale. It provides various capabilities like AutoAI, IBM SPSS Modeler, etc., to simplify your business data science and AI.
  • IBM Watson Machine Learning: It helps to make big data processes automated and more seamless. Using this, a data scientist can quickly begin training models and easily set up a cluster and launch the job within minutes.

IBM Analytics Engine offers a free trial version to test limited features first. You can choose from two payment models here: one is Lite = Free for a limited duration, the second is subscription-based= Hourly and Monthly plans. See the detailed pricing for the Analytics Engine.

Visit website: IBM

Also Read: Comparison of best software virtualization software in 2020: Hyper-V vs KVM vs vSphere vs XenServer

#5 Microsoft

Microsoft’s Big Data strategy is broad and provides a number of services to support it. Its focus is to allow users to gain actionable insights from virtually all data. Some time ago, Microsoft acquired Revolution Analytics which is an open-source big data analytics platform written in R programming language. It aims to help companies use it for unlocking big data insights with advanced analytics.

Microsoft’s advanced analytics architecture. Source: Microsoft

Microsoft has following Big Data solutions:

  • HDInsight: This is a Hadoop-based service by Microsoft, developed on the Hortonworks Data Platform (HDP) that provides complete compatibility with Hadoop. This allows you to gain insights from structured and unstructured data in almost any format or size, irrespective of its location.
  • Analytics Platform System: This service is fully integrated for data warehouse specific workloads. It delivers rapid insights from data through parallel processing across cloud and Hadoop clusters.

Microsoft’s range of big data offerings is vast, consisting of various supporting services. Blob Storage lets you store all types of unstructured data. Azure Synapse Analytics allows you to scale, compute, and store your data independently with parallel processing architecture.

If your organization needs an Apache Spark-based analytics platform, then Microsoft has Azure Databricks for you. Then, there is Data Lake Analytics to run huge parallel processing programs in different coding languages and store over petabytes of datasets in Azure Data Lake.

There is Power BI, business analytics tools that provide you insights throughout your organization.

See pricing details here. Visit website: Microsoft

In case you have questions, please add it in the comments section below.

Disclaimer: The information contained in this article is for general information purpose only. Price, product and feature information are subject to change. This information has been sourced from the websites and relevant resources available in the public domain of the named vendors on 23 June 2020. Wire19 makes best endeavors to ensure that the information is accurate and up to date, however, it does not warrant or guarantee that anything written here is 100% accurate, timely, or relevant to the website visitors.

Also Read: Top data analytics tools comparison: Alibaba Cloud, AWS, Azure, Google Cloud, IBM

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