Overview of AWS Data Lake
AWS provides Data Lake on AWS, which enables the deployment of a highly accessible, cost-effective data lake architecture on the AWS Cloud, as well as a user-friendly UI for searching and requesting datasets.
What is AWS Data Lake?
Amazon Web Services (AWS) data lake is a place to store data on the Cloud when it is ready for the Cloud. It can immediately locate in the lake with Amazon Glue, which maintains the data catalog. Before we get into the details, let us define a data lake.
What is Data Lake?
A data lake is a centralized repository that stores data from various sources in a raw, granular format. It is in the lake and can be structured, semi-structured, or unstructured. This enables it to be kept in a more flexible format for future use. When keeping it, lake associates it with metadata tags and identifiers for quicker retrieval.
What are the components of AWS Data Lake?
AWS Data Lake has the capability of storing almost unlimited data. Backup and Archive operations are optimized through Amazon Glacier. S3 object storage is where it is situated and is the cheapest on the Cloud. AWS Data Lake can be optimized with various AWS tools that can save costs up to 80% and can process jobs effectively on a 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, IoT, transactional or operational data, and so on. Once it is loaded to S3, then this can be used anytime and anywhere for all kinds of needs. The data in the lake may or may not be curated. Amazon S3 has a wide range of S3 classes for data storage. Each of them has its capabilities and securities. We can query in place using Amazon Athena and Redshift for processing.
Glacier for Backup and Archive
Amazon Glacier is a service on S3 that enables support for the secure Archiving of data and managing backups. Retrievals from current Archive stores are fast as they can access and retrieve 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, it sees all the datasets and loads its metadata, such as schema, to help query and search among that data faster. The purpose of Glue is to perform ETL operations on it. Glue is serverless; hence there is no infrastructure set up for it. This feature makes AWS glue more efficient and beneficial.
What are AWS Data Lake Analytics and its capabilities?
Amazon Web services have the capability of Analytics based on various market trends. AWS analytics is one of the broadest and most cost-effective services. 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 the best of its kind and is highly optimized to be deployed on Cloud.
Every cleaning of data and every coercion forms an opinion, It has none of that, which allows for innovative analytics in the future.Know more about : AWS Data Lake
Athena for Interactive Analytics
When it comes to Interactive analytics, data must be available and stored at a location where we can query it and have our interactive dashboards for its visualization. Amazon Athena provides a service that helps query data interactively and produces functional interactive analysis in S3 using standard SQL. Athena is serverless, and we only have to pay for queries we run on data. Athena allows users to write SQL queries for large datasets; there is no need to develop ETL jobs. Athena could be the best choice for any organization to integrate BI tools into S3 for visualization and Interactive Analytics.
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 a more sophisticated and well-formed Decision-making strategy for businesses to work for customers and earn more profit. Amazon Kinesis Data analytics helps to perform Analytics when input is immediately Available instead of loading it for hours and then processing that for analytics. When Media or other streaming data arrive at Kinesis Stream or Firehose-like endpoints for S3, it will become easy for Real-time Analytics. Amazon Kinesis is scalable enough to ingest data from thousands of sources.
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Elasticsearch service for Operational Analytics
Operational analytics is based on analyzing as much data as a machine can process to make more effective operational decisions for improving existing services or adopting a new service. For this, lots of searches, filters, and aggregations are required make, and Amazon Elasticsearch service helps to implement these operations on log data and clickstream data for monitoring and log analysis. Get an Insight into Data Lake Services for Real-Time
RedShift for Warehousing
Data warehousing is needed to query the petabytes for analytics, control, and ML-related operations. Amazon Redshift can run large, complex, and broad queries on data. It has a Redshift spectrum that can even run SQL queries on S3, reducing movement. It is cheaper of its kind 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 the Hadoop framework that can access other distributed frameworks such as Flink, Spark, etc. It allows easy and cost-effective discipline for the processing of defined tasks. Processing is performed on distributed and highly scalable Amazon EC2 instances. It processes data on Hadoop clusters on EC2 virtual servers (VPC). Amazon Sagemaker can be used for predictive analytics services related to Machine Learning. The Sagemaker platform can build, train, and deploy ML models on the go. It also works on EC2 instances with scalable infrastructure. Sagemaker is a platform service for ML developers that allows the visualization of training data on S3.
Why choose AWS for data lake and Analytics?
Choosing Services
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 this, running complex queries, and real-time analytics from any data file format. S3 can Store an Unlimited amount of curated or non-curated data.
Scalability as in Replications of data
AWS has an inbuilt data store as S3 that offers storage over multiple data centers of three different zones in a single AWS region as a replication, thus providing more scalability. It can replicate data between any part.
Amazon KMS for Security
AWS has a Key Management Service (AWS KMS) that manages data encryption as keys on server ends. An ML-based service, Amazon Macie, can be used for detecting attacks in 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 storing. S3 is the cheapest object storage; thus, using it to store data (Curated and non-curated) for different purposes also removes the overhead of Data movement and its cost of saving.Data lake Services and Solutions
AWS offers the most comprehensive set of analytics services to meet all of your data analytics requirements, allowing enterprises of all sizes and industries to reimagine their businesses with data.
Enhance the customer experience
With comprehensive, governed insights, you can comprehend and predict customer behaviors.
Streamline the process
Utilize various analytical and AI techniques to identify patterns and trends in
Control risk, compliance, and governance
Promote transparency and auditability with native data access powered by metadata in a governed lake.
Boost flexibility and output
Self-service data exploration and discovery for any user reduces time to value.
User, tool, and repository integration
The collaboration will improve, and managing various systems and tools in an integrated environment will take less time and money.
Utilize existing knowledge and open source
With enterprise-ready secure data lakes, you can transform your ecosystem and open-source investments into opportunities for innovation.
Cloud Adoption Approach
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 storing. S3 is the cheapest object storage; hence using it to store (Curated and non-curated) for different purposes also removes the overhead of Data movement and its cost of saving.
What's Next?
- Learn more about Building Data Lake using Apache NiFi
- Discover more about Data Lake Services for Real-Time Analytics
