It is a service offered by Amazon that helps in processing and providing insights to data by analysing it. There are many services in the analytics section which have been discussed below:
An interactive query service which helps in easy analysis of data stored in S3 with the help of SQL queries. It is server-less, hence having no infrastructure that needs to be managed. The user only pays for the queries they run.
It provides a Hadoop managed framework, which helps in quick and cost-effective method of processing large amounts of data across on-the-g- scalable EC2 instances. EMR notebooks are similar to Jupyternotebooks, and provide an environment to develop and collaborate for dynamic querying and exploratory analysis if data. It is highly-secure and offers reliability to handle a wide range of uses cases, which include, but are not limited to machine learning, scientific simulation, log analysis, web indexing, data transformations (ETL), and bioinformatics.
It is a service managed by AWS Cloud, and it makes the process of setting-up, managing and scaling a search solution for a website or an application an easy task. It supports 34 languages and comes with features such as highlighting, auto-complete, and geospatial searchability.
It helps ElasticSearch in the process of deploying, securing, operating and scaling data by searching, analysing and visualizing this data in real-time. It comes with easy-to-use APIs which can be used for log analysis, full-text search, scaling requirements, monitoring applications, clickstream analytics, and security. It can also be integrated with open-source tools such as Kibana, Logstash which help in ingesting data and visualization of data respectively.
It makes the process of collecting, processing, and analysing real-time data an easy task. This helps in achieving real-time insights to data so that this data can be analysed and actions can be taken based on the insights quickly. Real-time data including video, audio, application logs, website clickstreams, IoT telemetry data (meant for machine learning) can be ingested and this data can be responded to immediately in contrast to waiting for all the data to arrive before beginning pre-processing on it. It currently offers services like- Kinesis Data Firehose, Kinesis Data Analytics, Kinesis Data Streams, and Kinesis Video Streams.
It is a quick, and scalable data warehouse which helps in analysing user data in a cost-efficient and simple manner. It delivers 10 times quicker performance in comparison to other data warehousing services, since it uses machine learning, massively parallel query execution and columnar storage on high-performance disks. Petabytes of data can be queried upon, and a new data warehouse can be setup and deployed in a matter of minutes.
It is a cloud powered business intelligence service, which is fast, and helps in delivery insights to people in an organization. It allows users to create and publish interactive visual dashboards which can be accessed via mobile devices and browsers. These visual dashboards can be integrated with other applications thereby providing customers with powerful self-serving analysis service.
It helps process and move data between different AWS resources (compute and storage devices). Data can be regularly accessed from the place it is stored, it can be transformed and processed at scale. The result of this data processing can be transferred to other AWS services such as S3, RDS, DynamoDB, and EMR. It helps in the creation of complex data processing workloads which provide facilities such as high fault tolerance, high availability and repeatability.
It is a completely managed ETL service (Extract, Transform, Load) which helps users prepare and load their data for the purpose of analysis. An ETL job can be set up and run with a few mouse clicks from the AWS Management Console itself. Glue can be pointed to the location of data stored, and it discovers the data and its metadata and stores it in Glue Catalog. Once the data is in the catalog, it can be searched, queried, and made available for ETL process.
It is a service that helps in securing data lake. Data Lake can be visualised as a centralized, customized and secured data repository which stores this data in the original form as well as a processed form meant for data analysis. It helps combine various types of analytics that help in gaining deeper insights to data thereby helping make better business decisions. But the process of setting up and managing a data lake requires a lot of manual efforts. But Lake Formation makes this process an easy one by collecting data and cataloguing it. This data is then classified using ML algorithms as well as providing security for sensitive data.
It is a service that helps in building and running applications that use Apache Kafka. It is fully managed and helps process streaming data. Apache Kafka is open-source and is used to build real-time streaming data pipelines and application. With the help of MSK, Kafka API can be used to populate data lakes, reflect changes in the database and use machine learning to power other applications.
In this post, we understood the services which AWS Analytics offers that can be used in processing large amounts of data seamlessly.
Whoever has contributed to this article...I would like to say thank you... it has been of good help to the readers.
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