Co 4, session 2, aws analytics services
- 1. What are AWS Analytics Services? It is a service offered by Amazon that helps in processing and providing insights to data by analyzing it. There are many services in the analytics section which have been discussed below: https://forms.office.com/r/DKwT0JCEUZ
- 2. Amazon Athena • An interactive query engine which helps in easy analysis of data stored in S3 with the help of SQL queries. It is server-less (start query instantly), hence having no infrastructure that needs to be managed. The user only pays for the queries they run ($5 per terabyte scanned by your query). • Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL. Most results are delivered within seconds. With Athena, there’s no need for complex ETL jobs to prepare your data for analysis. This makes it easy for anyone with SQL skills to quickly analyze large-scale datasets. • Athena is out-of-the-box integrated with AWS Glue Data Catalog, allowing you to create a unified metadata repository across various services, crawl data sources to discover schemas and populate your Catalog with new and modified table and partition definitions, and maintain schema versioning.
- 3. Amazon EMR • An Industry-leading cloud big-data platform for processing huge amount of data using open source tools such as Apache Hadoop, Spark, Hive, Hbase, and Presto etc. • It makes it easy to setup, operate, and scale your big data environments by automating time consuming tasks like provisioning capacity and tuning clusters. • 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 Jupiter notebooks, 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.
- 4. Amazon CloudSearch • It is a service managed by AWS Cloud, • 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. • With ease of operation feature: features like auto-scaling, self-healing clusters, high availability and data durability • Easy administration: (security) built in integration with IAM and (auditing) built-in integration with Cloudtrail. • You don’t have to become a search expert or worry about h/w provisioning, setup and maintenance. • Offers pay-as-you-go pricing with no upfront expenses or long-term commitments. • Search endpoint, document endpoint, Document ARN, Searchable documents
- 5. Amazon ElasticSearch service • 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’s a open source search engine, writen in java language and is based on Apache Leucin. • It is real-time distributed & analytics engine • It supports full-text search instead of table/scheme. • It is document oriented
- 6. Amazon Kinesis • 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.
- 7. Amazon redshift • 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.
- 8. Amazon Quicksight • 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. • Functions of Quicksight (Business Intelligence tool) • Performance management: - • Data wrangling – Converting the data into a structured format (acquising, combining, cleansing) • Data mining – ETL • Data analytics • Data warehouse - • Predictive modelling
- 9. AWS data pipeline • 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.
- 10. AWS Glue • 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.
- 11. Amazon Lake Formation • 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.
- 12. Amazon Managed streaming for Kafka (MSK) • 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.
- 14. • Application Services (often used instead of application management services or application services management) are a pool of services such as load balancing, application performance monitoring, application acceleration, autoscaling, micro-segmentation, service proxy and service discovery needed to optimally deploy, run and improve applications.
- 15. What is Application Services Management? • The process of configuring, monitoring, optimizing and orchestrating different app services is known as application services management. • Today, organizations with their own data centers or which use the public cloud, handle applications services management. In the early days of online adoption, application service providers (or ASPs) were companies which would deliver applications to end users for a fixed cost. This single tenant, hosted model was largely replaced by the advent of the Software-as-a-Service (SaaS) delivery model which was multi-tenant and on- demand.
- 16. What is Cloud Application Services? • Cloud App Services are a wide range of specific application services for applications deployed in cloud-based resources. Services such as load balancing, application firewalling and service discovery can be achieved for applications running in private, public, hybrid or multi-cloud environments.
- 17. What are App Modernization Services? • Traditional applications were built as monolithic blocks of software. These monolithic applications have long life cycles because any changes or updates to one function, usually requires reconfiguring the entire application. This costly and time consuming process delays advancements and updates in application development. • Application Modernization Services enable the migration of monolithic, legacy application architectures to new application architectures that more closely match the business needs of modern enterprises’ application portfolio. Application modernization is often part of an organization’s digital transformation. • An example of this is the use of a microservices architecture where all app services are created individually and deployed separately from one another. This allows for scaling services based on specific business needs. Services can also be rapidly changed without affecting other parts of the application. Application-centric enterprises are choosing microservices architectures to take advantage of flexible container-based infrastructure models.