Overview of AWS Data Lake
Amazon Web Services (AWS) data lake is a place to store data on the cloud when data is ready for the cloud. It can immediately locate the data in Data lake with Amazon Glue that maintains the catalog of the data. AWS Data Lake has the capability of storing almost unlimited data. Backup and Archive operations are optimized through Amazon Glacier. S3 object storage is the place where data is situated, and it is the cheapest of its kind on the cloud. AWS Data Lake can be optimized with various AWS tools that can save costs up to 80% and can process job effectively on the scale. You can also explore Azure Data Lake Analytics capabilities in this. Some of the essential components that AWS data lake has been –
S3 object storage
Amazon Simple Storage Service (or, only S3) is object storage that can store any amount of data, any number of files on the cloud. S3 storage can store enterprise data, IoT data, transactional or operational data and so on. Once data is loaded to S3 then this data can be used anytime and anywhere for all kind of needs. The data in the Data lake may or may not be curated. Amazon S3 has a wide range of S3 classes to choose from for Data storage. Each of them has its capabilities and securities. We can query in-place by using Amazon Athena and Redshift for data processing.
Glacier for Backup and Archive
Amazon Glacier is a service on S3 than enables support for secure Archiving of data and managing backups. Retrievals of data form current Archive stores are very fast as they can access and retrieve data within 5 minutes. It archives the data across three availability zones within a region. The glacier is best suitable for use cases like asset delivery, healthcare information archiving and scientific data storage.
Glue for Data Catalog Operation
Amazon Glue is a Catalog management service that helps to find and catalog the metadata for faster queries and searches over data. Once we point Glue to the data stored in S3 Storage, Glue then sees all the data and loads its metadata such as schema that will help to query and search among that data faster. The purpose of Glue is performing ETL operations on data. Glue is serverless; hence there is no infrastructure set up for it. This feature makes AWS glue is more efficient and beneficial.
AWS Analytics and Its Capabilities
Amazon Web services have the capability of Analytics based on various market trends. AWS analytics is one of the broad and cost-effective services of its kind. It offers multiple services on the cloud such as Interactive Analytics, Operational Analytics, data warehousing, real-time analytics and many more. Every service offered by AWS analytics is best of its kind and is highly optimized to be deployed on Cloud.
Athena for Interactive Analytics
When it comes to Interactive analytics, data must be available and stored at a location from where we can query on it and have our interactive dashboards for data visualization. Amazon Athena provides a service that helps to interactively query on data and produce useful interactive analysis on data in S3 using standard SQL. Athena is serverless, and we only have to pay for queries that we run on data. Athena allows users to write SQL query for large datasets; there is no need to develop ETL jobs for it. If any organization wants to integrate BI tools to S3 for data visualization and Interactive Analytics, Athena could be the best choice.
Kinesis for Real-time Analytics
If we are not processing real-time data for Real-time analytics, then we are not working on big data. Real-time analytics provides more sophisticated and well-formed Decision-making strategy for businesses to work for customers and in-turn to earn more profit. Amazon Kinesis Data analytics helps to perform Analytics when data is immediately Available instead of loading data for hours and then process that data for analytics. When Media or other streaming data arrived at Kinesis Stream or Firehose like endpoints for S3, and then It will become easy to use that data for Real-time Analytics. Amazon Kinesis is scalable enough to ingest data from thousands of sources.
Elasticsearch service for Operational Analytics
Operational analytics is based on the concept to analyze as much data as a machine can process so that more effective operational decisions can be made for improving existing service or adopting new service. For this, lots of searches, filters, and aggregations are required to make, and Amazon Elasticsearch service helps to implement these operations on log data and clickstream data for monitoring and log analysis.
RedShift for Warehousing
Data warehousing is needed to querying the petabytes of data for analytics, control, and ML related operations. Amazon Redshift is capable of running large, complex and broad queries on data. It has a Redshift spectrum that can even run SQL queries on S3 data that reduces data movement. It is cheaper of its kind and than traditional tools also. We can scale it for $1000 Per terabyte per year. This provides the advantage of the cloud.
Using EMR for big data processing and Sagemaker for ML
Amazon has tools for Big data processing tasks such as Predictive analytics, Log analysis, Scientific solutions, and more under one hood. Amazon EMR has fully managed Hadoop framework that has the ability for other distributed frameworks such as Flink, Spark, etc. It allows easy and cost-effective discipline for data processing for defined tasks. Processing is performed on distributed and highly scalable Amazon EC2 instances. It processes data on Hadoop clusters on EC2 virtual servers (VPC). For Predictive analytics types of services that are related to Machine Learning, Amazon Sagemaker can be used. Sagemaker platform can build, train and deploy ML models on the go. It also works on EC2 instances with scalable infrastructure. Sagemaker works as a platform service for ML developers that allows visualizing training data on S3.
Why AWS data lake and Analytics?
AWS data lake and its Analytics services provide more opportunities for task-oriented services. It has different services available for various tasks or everyday tasks with more optimization and scalability such as Kinesis Streaming for Real-time Analytics, EMR for big data processing and many more. Though it is not just bounded to AWS itself, we can use AWS services from external applications also.
The flexibility of data formats
AWS has the flexibility for different data formats such as ORC, Parquet, Avro, CSV, and Grok. We can use standard SQL on AWS for processing of data, running complex queries, real-time analytics from any of the data file formats. S3 has the ability for storing Unlimited amount of curated or non-curated data.
Scalability as in Replications of data
AWS has inbuilt data store as S3 that offers storage over multiple data centers of three different zones in a single AWS region as replication of data thus providing more scalability. It can replicate data between any part.
Amazon KMS for Security
AWS has Key management Service (AWS KMS) that manages encryption of data as keys on server ends. An ML-based service, Amazon Macie, can be used for detecting attacks on their early stages and ensures no data theft will happen.
Cost effective storage
The most important reason one can use AWS is the cost of using AWS services for Data lakes and Analytics to Machine Learning use cases. AWS allows the user to manage services for their use cases in the most cost-effective manner that one has to pay for only querying not for storing. S3 is the cheapest object storage hence using it to store data (Curated data and non-curated data) for different purposes also removes the overhead of Data movement and its cost of saving.
Cloud Adoption Approach
With a centralized strategy for cloud adoption, Enterprises can minimize operations cost, reduce risks and achieve scalability of database capabilities. For building a cloud-enabled organization, we advise taking the following steps –
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