Data Lake on AWS
As the volume of customers’ data grows, companies realize the benefits that data has for their business.?Amazon Web Services (AWS)?offers many database and analytics services, which give companies the ability to build complex data management workloads. At the same time, these services can reduce operational overhead compared to traditional operations.
A data lake in AWS is a centralized repository where organized and unstructured data can be stored at any scale. It allows you to examine massive amounts of data from a variety of sources, including websites, mobile apps, IoT devices, and corporate applications, for various analytics and machine learning tasks. AWS offers a variety of services and tools for creating and managing data lakes, including Amazon S3, AWS Glue, AWS Lake Formation, Amazon Redshift, Amazon Athena, and Amazon EMR.
Why do you need a data lake?
Most businesses have a large amount of data created through various channels such as click-streams, internet-connected gadgets, social media, and log files, but many are confused about how to use it. According to an Aberdeen survey, enterprises who deployed a Data Lake outperformed similar companies by 9% in terms of organic revenue growth. This is because data lakes enable them to extract business value from their data in order to identify and act on chances for faster business growth.
Build a Data Lake on AWS
The AWS Cloud provides many of the building blocks required to help customers implement a secure, flexible, and cost-effective data lake. These include AWS managed services that help ingest, store, find, process, and analyze both structured and unstructured data. To support our customers as they build data lakes, AWS offers Data Lake on AWS, which deploys a highly available, cost-effective data lake architecture on the AWS Cloud along with a user-friendly console for searching and requesting datasets.
Data Lake on AWS automatically configures the key AWS services required to tag, search, share, transform, analyze, and govern specific subsets of data within an organization or with external users. The guidance implements a console that allows customers to search and browse various datasets for their business needs. It also provides a federated template, which allows you to launch a solution ready for integration with Microsoft Active Directory.
Layers of a data lake on AWS
Data lakes are composed of multiple layers. Below are the primary AWS services that we use inside a data lake:
·????? Data ingestion (Kinesis Firehose)
·????? ETL (glue)
·????? Storage (S3)
·????? Analytics (ERP)
·????? Security (IAM, STS, and KMS)
·????? Orchestration (Data Pipeline, Step Functions, and Amazon Managed Workflows for Apache Airflow (MWAA))
·????? Monitoring (CloudWatch and CloudTrail)
·????? Visualization (Athena and QuickSight)
Data Ingestion
Data ingestion is the first part of our architecture. Data lakes collect data a variety of sources, including internet-connected devices, click events, social media, and so on. In various ways, data originating from several channels can be consumed in the cloud. We use AWS Kinesis Firehose for this purpose. AWS Kinesis Firehose is Amazon's fully managed streaming solution for loading data into data lakes, storage, and analytics tools. In addition, it can batch, compress, alter, and encrypt the data before it is loaded. AWS Firehose is also scalable, which means you won't have to worry about ingesting capacity during peak periods in your system.
ETL
After ingestion, we modify our data to make it ready for analytics. We use the AWS Glue service for ETL operations. Amazon's completely managed, serverless ETL solution generates ETL jobs with minimal effort. Glue identifies our data and stores its related metadata (such as table definitions and structure) in the Glue Data Catalog. Once cataloged, our data is immediately searchable and ready for ETL. It is also feasible (and advised) to convert the data into columnar formats like Parquet or ORC in order to reduce costs and increase performance.
Storage
Storage is at the heart of a data lake. Because of the large amount of data (petabytes) being kept, the underlying data storage system must be scalable, dependable, and cost effective. To accomplish this, AWS Simple Storage Service (S3) is used. AWS S3 is an object storage service that provides scalability, data availability, security, and performance. We utilize AWS S3 to store our data instead of HDFS, which allows us to avoid constantly storing data in Hadoop clusters, improving scalability, dependability, and cost efficiency.
Analytics
There are numerous analytics solutions available in the market, depending on our clients' expertise and the tools they like to employ. Even so, Amazon's big data platform, EMR, has solutions for every case. AWS EMR includes the most popular open-source big data tools, including Apache Spark, Apache Hive, Apache HBase, Apache Flink, and Presto, as well as Amazon EC2's dynamic scaling and Amazon S3's scalable storage. EMR provides analytical teams with the horsepower and elasticity they need to execute petabyte-scale analyses at a fraction of the expense of typical on-premises clusters. Developers and analysts can use Jupyter-based EMR Notebooks for iterative development and testing.
Security
If you intend to store client data in the cloud, security becomes the most crucial consideration. All AWS services are GDPR-ready, which means that every service we are considering contemplate adopting includes encryption, access management, and monitoring features. The AWS Identity and Access Management (IAM) solution allows you to control access to all AWS services and resources. In addition, AWS Security Token Service (STS) allows you to request temporary, limited-privilege credentials (tokens) for IAM. Encryption is a critical part of keeping your data secure. AWS Key Management Service (KMS) allows you to manage your encryption keys. For encryption, you can alternatively use AWS managed keys or client-side encryption.
Monitoring
We use Amazon's monitoring services, AWS CloudWatch and AWS CloudTrail. CloudWatch allows you to monitor your apps, evaluate and respond to system-wide performance changes, optimize resource use, and gain a comprehensive perspective of operational health. CloudTrail allows us to track and record each API call made to the AWS environment. As a result, we can track all user activity on AWS services and resources.
Orchestration
Apache Airflow is an open-source tool for writing, scheduling, and monitoring processes. Amazon Managed Workflows for Apache Airflow (MWAA) is a managed orchestration service for Apache Airflow that simplifies the creation, operation, and scaling of end-to-end data pipelines without managing the underlying infrastructure for scalability, availability, and security. Amazon MWAA can help to cut operational costs and engineering expenses.
MWAA's auto scaling technique automatically increases the number of Apache Airflow workers in response to queued tasks and removes excess workers when no more activities are queued or running. You can configure task parallelism, auto scaling, and concurrency parameters straight from the MWAA console.
Amazon MWAA orchestrates and schedules operations with Python-based Directed Acyclic Graphs (DAGs). To execute DAGs in an Amazon MWAA environment, first copy your files to Amazon S3, then use the Amazon MWAA console to notify Amazon MWAA of the location of your DAGs and supporting files. Amazon MWAA synchronizes the DAGs among workers, schedulers, and the web server.
Visualization
Finally, visualization is a topic for a data lake. Business analysts or data scientists require a visual dashboard to see and analyze workflow output. Most organizations pay a lot of money for popular BI products, however with AWS services, you don't have to pay license costs. Amazon QuickSight is a quick, cloud-based business intelligence service that makes it simple to share insights with everyone in your organization. QuickSight, a fully managed service, enables you to easily construct and publish interactive dashboards with ML Insights. Dashboards can be accessible from any device and embedded in your applications, portals, and websites.
You can also regulate or limit user access to dashboards to ensure the right level of data protection. Amazon Athena is another excellent visualization tool that we use in AWS. It is an interactive query service that allows you to easily analyze data in Amazon S3 using SQL. Athena is totally managed and serverless, which means there is no infrastructure to manage, and you just pay for the queries you run. Athena is straightforward to use. You can simply point to your Amazon S3 data, set the schema, and query with ordinary SQL.
This allows anyone with SQL skills to quickly analyze huge datasets. Athena is also linked with AWS Glue Data Catalog, which allows you to construct a uniform metadata repository across many services, explore data sources to locate schemas, populate your Catalog with new and amended table and partition definitions, and manage schema versioning.
Key Takeaways
As customer data volumes grow, companies are increasingly recognizing the benefits of this data for their business. Commencis enables its clients to utilize AWS services efficiently and securely, which supports complex data management workloads and reduces operational overhead. These services facilitate the creation and management of data lakes—a centralized repository that allows for the storage of organized and unstructured data at any scale.
References:
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