Big Data Services: AWS VS. Azurevs. google

"TechTarget China original" for enterprise users, big Data service is a more attractive cloud service. The Big Three, AWS, Azure and Google, are trying to win the top spot, but in the end, which one can win the Battle of the throne? The cloud market is growing fast and the same big data services are constantly changing. Although the starting point for the big Three cloud providers (Amazon Web Services, Microsoft Azure, and Google) is different, this makes comparisons between cloud providers more difficult, but it's worth trying. Cloud Big Data is a market area where Google has always had a synergistic effect on search applications, but Amazon Web Services (AWS) and Azure will attract some interesting startups to improve their competitiveness. As a result, big data services are becoming more attractive due to their functional and economic reasons, thus gaining good prospects for development. Cloud users will be the last winners in the Big Three big Data Service application battle, but the long-drawn war seems to persist for years. Let's take a brief introduction to the three today's big data Services for AWS, Azure and Google. Amazon Web Services AWS offers a very broad range of big data services. For example, Amazon elastic MapReduce can run Hadoop and Spark, while Kinesis Firehose and Kinesis Streams provide a way to import large datasets into AWS. Users can store data in redshift, which is a petabyte-scale data Warehouse, and compare data to achieve cost reductions. Amazon Elasticsearch is a service that deploys open source Elasticsearch tools in AWS for analytics applications such as CTR and log monitoring. Kinesis Analytics can help achieve this by analyzing data flow. Unlike Google, AWS offers a complete set of larger data storage options. In addition to a large number of AWS simple storage services, it also provides a low latency NoSQL database Dynamodb;dynamodb's Titan version to provide storage services for the Titan graphics database; Apachehbase is a petabyte-scale NoSQL Database ; and relational databases. AWS also provides a business intelligence (BI) service, Quicksight, which uses in-memory parallel processing technology for high-speed operation. It is implemented primarily through the Amazon machine learning and IoT platform, which connects many devices to the cloud and expands the number of devices connected to 1 billion and processes trillions of levels of messages. Overall, while Google has a big advantage in search and analysis engines, AWS has a broader range of services, BI, and graphics processing Unit (GPU) instances. Microsoft AZure for analytics applications, Azure has data Lake Analytics, which uses dedicated U-SQL and C + + and a Hadoop-based service hdinsight. There is also an Azure Stream analytics service, which has a data Catalog that uses the global metadata system to identify the asset, and data Factory that connects the internal and cloud data sources and manages the data pipeline. Azure's Big Data storage service is a Dadoop file system called the Data Lake store. This cloud service provider offers a variety of common storage products, including Storsimple, SQL and NoSQL databases, and storage blocks. Azure also works with power bi and machine learning services, and has an Internet of things center. Its cloud platform also includes a search engine. Microsoft's Cortana suite and cognitive services provide more advanced intelligence capabilities. Google Google's BigQuery data service uses a majority of users (even non-technical) to intuitively learn to use a SQL-like interface. It supports a petabyte-scale database that can stream data at 100,000 rows per second and serves as an alternative to running data in cloud storage. BigQuery also supports geo-data replication, where users can choose where to store their data. BigQuery is a pay-as-you-go service that does not require a dedicated infrastructure instance, allowing Google to use a large number of processors to maintain a low latency, fast query response. Integrated with Spark, it also supports Hadoop, pig, and hive. Enterprise users can also use Google Analytics and DoubleClick as a data source, a tool for advertising users to collect data for bigquery use. Google's cloud dataflow also allows users to sort cloud data services. Other big Data services offered by Google include a NoSQL database for non-relational data cloud Datastore; a massively extensible NoSQL Database Cloud BigTable; a managed platform for machine learning applications Cloud machines Learning, and auxiliary tools such as translators and voice converters. One of the products that Google clearly lacks in big data services is GPU instances. Given the incredible performance gains of the GPU, writing GPU code for data analytics applications is really a high value-added skill. Google's lack of GPU-instance product lines is a bit confusing, especially since AWS launched the service in 2011, while Azure has 2015The service was added in the year. AWS, Azure, and Google: a race each other big Data app competition in many aspects of big data Services, the three giants of cloud vendors are unison, but there are some differences in performance and usability that need to be differentiated by actual testing. While Google may have some advantage in search technology, it lags behind in bi front-end applications, and Microsoft, which has Cortana, has a head start. Google's lack of GPU instances is also a significant difference. Because big data services are so diverse, and all products are in the early stages of life, the differences between them can vary depending on the use case or data type. It would be difficult to make a choice among the big three. One way to determine the best fit for your own cloud service is to use the sandbox for a few weeks to try out the services in order to master their first-hand experience and price information.

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Big Data Services: AWS VS. Azurevs. google