#AWS Data Engineer

[innovaticsblog]

Unleash the power of the cloud. Our cloud consulting strategy helps you design a secure, scalable, and cost-effective cloud roadmap aligned with your business goals.

[antstackinc]

Serverless-native AWS Data Engineer in Bangalore — Antstack

Modernize your applications for the digital age. Embrace cloud-native and serverless architecture and advanced technologies for increased scalability and flexibility. We transform conventional applications into a modern architecture that leverages serverless computing. Our AWS data engineer effort doesn’t become a ‘legacy’ when we complete it!

[avdgroupin]

AWS Data Engineer: Comprehensive Guide to Your New Career

Become an expert AWS data engineer by signing up for an AWS data engineering course in Pune. Join AVD Group for expert guidance!

[siri0007]

Introduction to AWS Data Engineering: Key Services and Use Cases 

Introduction 

Business operations today generate huge datasets which need significant amounts of processing during each operation. Data handling efficiency is essential for organization decision making and expansion initiatives. Through its cloud solutions known as Amazon Web Services (AWS) organizations gain multiple data-handling platforms which construct protected and scalable data pipelines at affordable rates. AWS data engineering solutions enable organizations to both acquire and store data and perform analytical tasks and machine learning operations. A suite of services allows business implementation of operational workflows while organizations reduce costs and boost operational efficiency and maintain both security measures and regulatory compliance. The article presents basic details about AWS data engineering solutions through their practical applications and actual business scenarios. 

What is AWS Data Engineering? 

AWS data engineering involves designing, building, and maintaining data pipelines using AWS services. It includes: 

Data Ingestion: Collecting data from sources such as IoT devices, databases, and logs. 

Data Storage: Storing structured and unstructured data in a scalable, cost-effective manner. 

Data Processing: Transforming and preparing data for analysis. 

Data Analytics: Gaining insights from processed data through reporting and visualization tools. 

Machine Learning: Using AI-driven models to generate predictions and automate decision-making. 

With AWS, organizations can streamline these processes, ensuring high availability, scalability, and flexibility in managing large datasets. 

Key AWS Data Engineering Services 

AWS provides a comprehensive range of services tailored to different aspects of data engineering. 

Amazon S3 (Simple Storage Service) – Data Storage 

Amazon S3 is a scalable object storage service that allows organizations to store structured and unstructured data. It is highly durable, offers lifecycle management features, and integrates seamlessly with AWS analytics and machine learning services. 

Supports unlimited storage capacity for structured and unstructured data. 

Allows lifecycle policies for cost optimization through tiered storage. 

Provides strong integration with analytics and big data processing tools. 

Use Case: Companies use Amazon S3 to store raw log files, multimedia content, and IoT data before processing. 

AWS Glue – Data ETL (Extract, Transform, Load) 

AWS Glue is a fully managed ETL service that simplifies data preparation and movement across different storage solutions. It enables users to clean, catalog, and transform data automatically. 

Supports automatic schema discovery and metadata management. 

Offers a serverless environment for running ETL jobs. 

Uses Python and Spark-based transformations for scalable data processing. 

Use Case: AWS Glue is widely used to transform raw data before loading it into data warehouses like Amazon Redshift. 

Amazon Redshift – Data Warehousing and Analytics 

Amazon Redshift is a cloud data warehouse optimized for large-scale data analysis. It enables organizations to perform complex queries on structured datasets quickly. 

Uses columnar storage for high-performance querying. 

Supports Massively Parallel Processing (MPP) for handling big data workloads. 

It integrates with business intelligence tools like Amazon QuickSight. 

Use Case: E-commerce companies use Amazon Redshift for customer behavior analysis and sales trend forecasting. 

Amazon Kinesis – Real-Time Data Streaming 

Amazon Kinesis allows organizations to ingest, process, and analyze streaming data in real-time. It is useful for applications that require continuous monitoring and real-time decision-making. 

Supports high-throughput data ingestion from logs, clickstreams, and IoT devices. 

Works with AWS Lambda, Amazon Redshift, and Amazon Elasticsearch for analytics. 

Enables real-time anomaly detection and monitoring. 

Use Case: Financial institutions use Kinesis to detect fraudulent transactions in real-time. 

AWS Lambda – Serverless Data Processing 

AWS Lambda enables event-driven serverless computing. It allows users to execute code in response to triggers without provisioning or managing servers. 

Executes code automatically in response to AWS events. 

Supports seamless integration with S3, DynamoDB, and Kinesis. 

Charges only for the compute time used. 

Use Case: Lambda is commonly used for processing image uploads and extracting metadata automatically. 

Amazon DynamoDB – NoSQL Database for Fast Applications 

Amazon DynamoDB is a managed NoSQL database that delivers high performance for applications that require real-time data access. 

Provides single-digit millisecond latency for high-speed transactions. 

Offers built-in security, backup, and multi-region replication. 

Scales automatically to handle millions of requests per second. 

Use Case: Gaming companies use DynamoDB to store real-time player progress and game states. 

Amazon Athena – Serverless SQL Analytics 

Amazon Athena is a serverless query service that allows users to analyze data stored in Amazon S3 using SQL. 

Eliminates the need for infrastructure setup and maintenance. 

Uses Presto and Hive for high-performance querying. 

Charges only for the amount of data scanned. 

Use Case: Organizations use Athena to analyze and generate reports from large log files stored in S3. 

AWS Data Engineering Use Cases 

AWS data engineering services cater to a variety of industries and applications. 

Healthcare: Storing and processing patient data for predictive analytics. 

Finance: Real-time fraud detection and compliance reporting. 

Retail: Personalizing product recommendations using machine learning models. 

IoT and Smart Cities: Managing and analyzing data from connected devices. 

Media and Entertainment: Streaming analytics for audience engagement insights. 

These services empower businesses to build efficient, scalable, and secure data pipelines while reducing operational costs. 

Conclusion 

AWS provides a comprehensive ecosystem of data engineering tools that streamline data ingestion, storage, transformation, analytics, and machine learning. Services like Amazon S3, AWS Glue, Redshift, Kinesis, and Lambda allow businesses to build scalable, cost-effective, and high-performance data pipelines. 

Selecting the right AWS services depends on the specific needs of an organization. For those looking to store vast amounts of unstructured data, Amazon S3 is an ideal choice. Companies needing high-speed data processing can benefit from AWS Glue and Redshift. Real-time data streaming can be efficiently managed with Kinesis. Meanwhile, AWS Lambda simplifies event-driven processing without requiring infrastructure management. 

Understanding these AWS data engineering services allows businesses to build modern, cloud-based data architectures that enhance efficiency, security, and performance. 

References 

For further reading, refer to these sources: 

AWS Prescriptive Guidance on Data Engineering 

AWS Big Data Use Cases 

Key AWS Services for Data Engineering Projects 

Top 10 AWS Services for Data Engineering 

AWS Data Engineering Essentials Guidebook 

AWS Data Engineering Guide: Everything You Need to Know 

Exploring Data Engineering Services in AWS 

By leveraging AWS data engineering services, organizations can transform raw data into valuable insights, enabling better decision-making and competitive advantage. 

[quantumworks]

AWS Data Engineer Training | AWS Data Engineer Online Course.

​AccentFuture offers an expert-led online AWS Data Engineer training program designed to help you master data integration, analytics, and cloud solutions on Amazon Web Services (AWS). This comprehensive course covers essential topics such as data ingestion, storage solutions, ETL processes, real-time data processing, and data analytics using key AWS services like S3, Glue, Redshift, Kinesis, and more. The curriculum is structured into modules that include hands-on projects and real-world applications, ensuring practical experience in building and managing data pipelines on AWS. Whether you're a beginner or an IT professional aiming to enhance your cloud skills, this training provides the knowledge and expertise needed to excel in cloud data engineering.

For more information and to enroll, visit AccentFuture's official course page.

[awsaicourse12]

AWS Data Analytics Training | AWS Data Engineering Training in Bangalore

What’s the Most Efficient Way to Ingest Real-Time Data Using AWS?

AWS provides a suite of services designed to handle high-velocity, real-time data ingestion efficiently. In this article, we explore the best approaches and services AWS offers to build a scalable, real-time data ingestion pipeline.

Understanding Real-Time Data Ingestion

Real-time data ingestion involves capturing, processing, and storing data as it is generated, with minimal latency. This is essential for applications like fraud detection, IoT monitoring, live analytics, and real-time dashboards. AWS Data Engineering Course

Key Challenges in Real-Time Data Ingestion

Scalability – Handling large volumes of streaming data without performance degradation.

Latency – Ensuring minimal delay in data processing and ingestion.

Data Durability – Preventing data loss and ensuring reliability.

Cost Optimization – Managing costs while maintaining high throughput.

Security – Protecting data in transit and at rest.

AWS Services for Real-Time Data Ingestion

1. Amazon Kinesis

Kinesis Data Streams (KDS): A highly scalable service for ingesting real-time streaming data from various sources.

Kinesis Data Firehose: A fully managed service that delivers streaming data to destinations like S3, Redshift, or OpenSearch Service.

Kinesis Data Analytics: A service for processing and analyzing streaming data using SQL.

Use Case: Ideal for processing logs, telemetry data, clickstreams, and IoT data.

2. AWS Managed Kafka (Amazon MSK)

Amazon MSK provides a fully managed Apache Kafka service, allowing seamless data streaming and ingestion at scale.

Use Case: Suitable for applications requiring low-latency event streaming, message brokering, and high availability.

3. AWS IoT Core

For IoT applications, AWS IoT Core enables secure and scalable real-time ingestion of data from connected devices.

Use Case: Best for real-time telemetry, device status monitoring, and sensor data streaming.

4. Amazon S3 with Event Notifications

Amazon S3 can be used as a real-time ingestion target when paired with event notifications, triggering AWS Lambda, SNS, or SQS to process newly added data.

Use Case: Ideal for ingesting and processing batch data with near real-time updates.

5. AWS Lambda for Event-Driven Processing

AWS Lambda can process incoming data in real-time by responding to events from Kinesis, S3, DynamoDB Streams, and more. AWS Data Engineer certification

Use Case: Best for serverless event processing without managing infrastructure.

6. Amazon DynamoDB Streams

DynamoDB Streams captures real-time changes to a DynamoDB table and can integrate with AWS Lambda for further processing.

Use Case: Effective for real-time notifications, analytics, and microservices.

Building an Efficient AWS Real-Time Data Ingestion Pipeline

Step 1: Identify Data Sources and Requirements

Determine the data sources (IoT devices, logs, web applications, etc.).

Define latency requirements (milliseconds, seconds, or near real-time?).

Understand data volume and processing needs.

Step 2: Choose the Right AWS Service

For high-throughput, scalable ingestion → Amazon Kinesis or MSK.

For IoT data ingestion → AWS IoT Core.

For event-driven processing → Lambda with DynamoDB Streams or S3 Events.

Step 3: Implement Real-Time Processing and Transformation

Use Kinesis Data Analytics or AWS Lambda to filter, transform, and analyze data.

Store processed data in Amazon S3, Redshift, or OpenSearch Service for further analysis.

Step 4: Optimize for Performance and Cost

Enable auto-scaling in Kinesis or MSK to handle traffic spikes.

Use Kinesis Firehose to buffer and batch data before storing it in S3, reducing costs.

Implement data compression and partitioning strategies in storage. AWS Data Engineering online training

Step 5: Secure and Monitor the Pipeline

Use AWS Identity and Access Management (IAM) for fine-grained access control.

Monitor ingestion performance with Amazon CloudWatch and AWS X-Ray.

Best Practices for AWS Real-Time Data Ingestion

Choose the Right Service: Select an AWS service that aligns with your data velocity and business needs.

Use Serverless Architectures: Reduce operational overhead with Lambda and managed services like Kinesis Firehose.

Enable Auto-Scaling: Ensure scalability by using Kinesis auto-scaling and Kafka partitioning.

Minimize Costs: Optimize data batching, compression, and retention policies.

Ensure Security and Compliance: Implement encryption, access controls, and AWS security best practices. AWS Data Engineer online course

Conclusion

AWS provides a comprehensive set of services to efficiently ingest real-time data for various use cases, from IoT applications to big data analytics. By leveraging Amazon Kinesis, AWS IoT Core, MSK, Lambda, and DynamoDB Streams, businesses can build scalable, low-latency, and cost-effective data pipelines. The key to success is choosing the right services, optimizing performance, and ensuring security to handle real-time data ingestion effectively.

Would you like more details on a specific AWS service or implementation example? Let me know!

Visualpath is Leading Best AWS Data Engineering training.Get an offering Data Engineering course in Hyderabad.With experienced,real-time trainers.And real-time projects to help students gain practical skills and interview skills.We are providing  24/7 Access to Recorded Sessions  ,For more information,call on +91-7032290546

For more information About AWS Data Engineering training

Call/WhatsApp: +91-7032290546

Visit: https://www.visualpath.in/online-aws-data-engineering-course.html

[azuresolutionsarchitect12]

AWS Data Engineering online training | AWS Data Engineer

AWS Data Engineering: An Overview and Its Importance

Introduction

AWS Data Engineering plays a significant role in handling and transforming raw data into valuable insights using Amazon Web Services (AWS) tools and technologies. This article explores AWS Data Engineering, its components, and why it is essential for modern enterprises. In today's data-driven world, organizations generate vast amounts of data daily. Effectively managing, processing, and analyzing this data is crucial for decision-making and business growth. AWS Data Engineering Training

What is AWS Data Engineering?

AWS Data Engineering refers to the process of designing, building, and managing scalable and secure data pipelines using AWS cloud services. It involves the extraction, transformation, and loading (ETL) of data from various sources into a centralized storage or data warehouse for analysis and reporting. Data engineers leverage AWS tools such as AWS Glue, Amazon Redshift, AWS Lambda, Amazon S3, AWS Data Pipeline, and Amazon EMR to streamline data processing and management.

Key Components of AWS Data Engineering

AWS offers a comprehensive set of tools and services to support data engineering. Here are some of the essential components:

Amazon S3 (Simple Storage Service): A scalable object storage service used to store raw and processed data securely.

AWS Glue: A fully managed ETL (Extract, Transform, Load) service that automates data preparation and transformation.

Amazon Redshift: A cloud data warehouse that enables efficient querying and analysis of large datasets. AWS Data Engineering Training

AWS Lambda: A serverless computing service used to run functions in response to events, often used for real-time data processing.

Amazon EMR (Elastic MapReduce): A service for processing big data using frameworks like Apache Spark and Hadoop.

AWS Data Pipeline: A managed service for automating data movement and transformation between AWS services and on-premise data sources.

AWS Kinesis: A real-time data streaming service that allows businesses to collect, process, and analyze data in real time.

Why is AWS Data Engineering Important?

AWS Data Engineering is essential for businesses due to several key reasons: AWS Data Engineering Training Institute

Scalability and Performance AWS provides scalable solutions that allow organizations to handle large volumes of data efficiently. Services like Amazon Redshift and EMR ensure high-performance data processing and analysis.

Cost-Effectiveness AWS offers pay-as-you-go pricing models, eliminating the need for large upfront investments in infrastructure. Businesses can optimize costs by only using the resources they need.

Security and Compliance AWS provides robust security features, including encryption, identity and access management (IAM), and compliance with industry standards like GDPR and HIPAA. AWS Data Engineering online training

Seamless Integration AWS services integrate seamlessly with third-party tools and on-premise data sources, making it easier to build and manage data pipelines.

Real-Time Data Processing AWS supports real-time data processing with services like AWS Kinesis and AWS Lambda, enabling businesses to react to events and insights instantly.

Data-Driven Decision Making With powerful data engineering tools, organizations can transform raw data into actionable insights, leading to improved business strategies and customer experiences.

Conclusion

AWS Data Engineering is a critical discipline for modern enterprises looking to leverage data for growth and innovation. By utilizing AWS's vast array of services, organizations can efficiently manage data pipelines, enhance security, reduce costs, and improve decision-making. As the demand for data engineering continues to rise, businesses investing in AWS Data Engineering gain a competitive edge in the ever-evolving digital landscape.

Visualpath is the Best Software Online Training Institute in Hyderabad. Avail complete AWS Data Engineering Training worldwide. You will get the best course at an affordable cost

Visit: https://www.visualpath.in/online-aws-data-engineering-course.html

Visit Blog: https://visualpathblogs.com/category/aws-data-engineering-with-data-analytics/

WhatsApp: https://www.whatsapp.com/catalog/919989971070/

[ms-demeanor]

Do you have thoughts about the changes to Firefox's Terms of Use and Privacy Notice? A lot of people seem to be freaking out ("This is like when google removed 'Don't be evil!'"), but it seems to me like just another case of people getting confused by legalese.

Yeah you got it in one.

I've been trying not to get too fighty about it so thank you for giving me the excuse to talk about it neutrally and not while arguing with someone.

Firefox sits in such an awful place when it comes to how people who understand technology at varying levels interact with it.

On one very extreme end you've got people who are pissed that Firefox won't let you install known malicious extensions because that's too controlling of the user experience; these are also the people who tend to say that firefox might as well be spyware because they are paid by google to have google as the default search engine for the browser.

In the middle you've got a bunch of people who know a little bit about technology - enough to know that they should be suspicious of it - but who are only passingly familiar with stuff like "internet protocols" and "security certificates" and "legal liability" who see every change that isn't explicitly about data anonymization as a threat that needs to be killed with fire. These are the people who tend not to know that you can change the data collection settings in Firefox.

And on the other extreme you've got people who are pretty sure that firefox is a witch and that you're going to get a virus if you download a browser that isn't chrome so they won't touch Firefox with a ten foot pole.

And it's just kind of exhausting. It reminds me of when you've got people who get more mad at queer creators for inelegantly supporting a cause than they are at blatant homophobes. Like, yeah, you focus on the people whose minds you can change, and Firefox is certainly more responsive to user feedback than Chrome, but also getting you to legally agree that you won't sue Firefox for temporarily storing a photo you're uploading isn't a sign that Firefox sold out and is collecting all your data to feed to whichever LLM is currently supposed to be pouring the most bottles of water into landfills before pissing in the plastic bottle and putting the plastic bottle full of urine in the landfill.

The post I keep seeing (and it's not one post, i've seen this in youtube comment sections and on discord and on tumblr) is:

Well-meaning person who has gotten the wrong end of the stick: This is it, go switch to sanguinetapir now, firefox has gone to the dark side and is selling your data. [Link to *an internet comment section* and/or redditor reactions as evidence of wrongdoing].

Response: I think you may be misreading the statements here, there's been an update about this and everything.

Well-meaning (and deeply annoying) person who has gotten the wrong end of the stick: If you'd read the link you'd see that actually no I didn't misinterpret this, as evidenced by the dozens of commenters on this other site who are misinterpreting the ToU the same way that I am, but more snarkily.

Bud.

Anyway the consensus from the actual security nerds is "jesus fucking christ we carry GPS locators in our pockets all goddamned day and there are cameras everywhere and there is a long-lasting global push to erode the right to encrypt your data and facebook is creating tracking accounts for people who don't even have a facebook and they are giving data about abortion travel to the goddamned police state" and they could not be reached for comment about whether Firefox is bad now, actually, because they collect anonymized data about the people who use pocket.

My response is that there is a simple fix for all of this and it is to walk into the sea.

(I am not worried about the updated firefox ToU, I personally have a fair amount of data collection enabled on my browser because I do actually want crash reports to go to firefox when my browser crashes; however i'm not actually all that worried about firefox collecting, like, ad data on me because I haven't seen an ad in ten years and if one popped up on my browser i'd smash my screen with a stand mixer - I don't care about location data either because turning on location on your devices is for suckers but also *the way the internet works means unless you're using a traffic anonymizer at all times your browser/isp/websites you connect to/vpn/what fucking ever know where you are because of the IP address that they *have* to be able to see to deliver the internet to you and that is, generally speaking, logged as a matter of course by the systems that interact with it*)

Anyway if you're worried about firefox collecting your data you should ABSOLUTELY NOT BE ON DISCORD OR YOUTUBE and if you are on either of those things you should 100% be using them in a browser instead of an app and i don't particularly care if that browser is firefox or tonsilferret but it should be one with an extension that allows you to choose what data gets shared with the sites it interacts with.

[aretovetechnologies01]

Aretove Technologies specializes in data science consulting and predictive analytics, particularly in healthcare. We harness advanced data analytics to optimize patient care, operational efficiency, and strategic decision-making. Our tailored solutions empower healthcare providers to leverage data for improved outcomes and cost-effectiveness. Trust Aretove Technologies for cutting-edge predictive analytics and data-driven insights that transform healthcare delivery.

[gudguy1a]

Lack of Success in the AWS Data Engineer Job Market

Wow! Talk about disappointment, the job market is definitely tough right now for AWS Data Engineers. Or, Data Engineers overall. The oddest part though, ~85% of the emails/calls I receive, they are for Senior or Lead Data Engineer and/or Data Scientist roles. When I am trying to break in at the mid-level Data Engineer role because I know I do not yet have the Senior level experience yet. But…

[phonegap]

How to create a Redshift Cluster? Learn how to create a Redshift cluster effortlessly. Explore the process of setting up a Redshift cluster through the AWS console, managing Redshift Processing Units (RPUs), and optimizing cluster performance.

[evolvecloudlabs]

This blog will delve into the benefits of cloud data engineering, its significance in our data-driven world, key factors to consider during its implementation, and the pivotal role of Google-certified professional data engineers in this domain.

[nitor-infotech]

In today's fast-paced digital landscape, cloud technology has emerged as a transformative force that empowers organizations to innovate, scale and adapt like never before. Learn more about our services, go through our blogs, study materials, case studies - https://bit.ly/463FjrO

[azuresolutionsarchitect12]

AWS Data Engineering | AWS Data Engineer online course

Key AWS Services Used in Data Engineering

AWS data engineering solutions are essential for organizations looking to process, store, and analyze vast datasets efficiently in the era of big data. Amazon Web Services (AWS) provides a wide range of cloud services designed to support data engineering tasks such as ingestion, transformation, storage, and analytics. These services are crucial for building scalable, robust data pipelines that handle massive datasets with ease. Below are the key AWS services commonly utilized in data engineering: AWS Data Engineer Certification

1. AWS Glue

AWS Glue is a fully managed extract, transform, and load (ETL) service that helps automate data preparation for analytics. It provides a serverless environment for data integration, allowing engineers to discover, catalog, clean, and transform data from various sources. Glue supports Python and Scala scripts and integrates seamlessly with AWS analytics tools like Amazon Athena and Amazon Redshift.

2. Amazon S3 (Simple Storage Service)

Amazon S3 is a highly scalable object storage service used for storing raw, processed, and structured data. It supports data lakes, enabling data engineers to store vast amounts of unstructured and structured data. With features like versioning, lifecycle policies, and integration with AWS Lake Formation, S3 is a critical component in modern data architectures. AWS Data Engineering online training

3. Amazon Redshift

Amazon Redshift is a fully managed, petabyte-scale data warehouse solution designed for high-performance analytics. It allows organizations to execute complex queries and perform real-time data analysis using SQL. With features like Redshift Spectrum, users can query data directly from S3 without loading it into the warehouse, improving efficiency and reducing costs.

4. Amazon Kinesis

Amazon Kinesis provides real-time data streaming and processing capabilities. It includes multiple services:

Kinesis Data Streams for ingesting real-time data from sources like IoT devices and applications.

Kinesis Data Firehose for streaming data directly into AWS storage and analytics services.

Kinesis Data Analytics for real-time analytics using SQL.

Kinesis is widely used for log analysis, fraud detection, and real-time monitoring applications.

5. AWS Lambda

AWS Lambda is a serverless computing service that allows engineers to run code in response to events without managing infrastructure. It integrates well with data pipelines by processing and transforming incoming data from sources like Kinesis, S3, and DynamoDB before storing or analyzing it. AWS Data Engineering Course

6. Amazon DynamoDB

Amazon DynamoDB is a NoSQL database service designed for fast and scalable key-value and document storage. It is commonly used for real-time applications, session management, and metadata storage in data pipelines. Its automatic scaling and built-in security features make it ideal for modern data engineering workflows.

7. AWS Data Pipeline

AWS Data Pipeline is a data workflow orchestration service that automates the movement and transformation of data across AWS services. It supports scheduled data workflows and integrates with S3, RDS, DynamoDB, and Redshift, helping engineers manage complex data processing tasks.

8. Amazon EMR (Elastic MapReduce)

Amazon EMR is a cloud-based big data platform that allows users to run large-scale distributed data processing frameworks like Apache Hadoop, Spark, and Presto. It is used for processing large datasets, performing machine learning tasks, and running batch analytics at scale.

9. AWS Step Functions

AWS Step Functions help in building serverless workflows by coordinating AWS services such as Lambda, Glue, and DynamoDB. It simplifies the orchestration of data processing tasks and ensures fault-tolerant, scalable workflows for data engineering pipelines. AWS Data Engineering Training

10. Amazon Athena

Amazon Athena is an interactive query service that allows users to run SQL queries on data stored in Amazon S3. It eliminates the need for complex ETL jobs and is widely used for ad-hoc querying and analytics on structured and semi-structured data.

Conclusion

AWS provides a powerful ecosystem of services that cater to different aspects of data engineering. From data ingestion with Kinesis to transformation with Glue, storage with S3, and analytics with Redshift and Athena, AWS enables scalable and cost-efficient data solutions. By leveraging these services, data engineers can build resilient, high-performance data pipelines that support modern analytics and machine learning workloads.

Visualpath is the Best Software Online Training Institute in Hyderabad. Avail complete AWS Data Engineering Training worldwide. You will get the best course at an affordable cost.

[purinfelix]

congrats on 1k!!! could i request a hot cocoa for oscar piastri with ever seen?

your event is so so cute <33

the prettiest eyes he's ever seen ⟡ ݁₊ . - oscar piastri

a/n: okay normally i would've wanted a more detailed req but as soon as i read this i instantly had an idea so u get off this time <333 hope u enjoy

this is part of my 1k event - check out the rules here!!

"My gosh, this is uncomfortable," you laugh from the seat of Oscar's race car.

"Well, I only have to sit in there for about an hour and a half at a time," he explains matter-of-factly.

Around you, the McLaren garage is alive with people hurrying around - engineers making sure the last parts are in place before the race, strategists going over data, and even a couple media crew snapping photos. And then there was you and your boyfriend, who had decided that your visit to the garage would be incomplete without sitting in his car.

"It's digging into my butt," you complain, "how do you even do this."

"Well it is my job, baby" he laughs, watching you with an endeared look.

"Yeah, and there's a reason it isn't mine, can I get out now?"

"Wait, wait!" he stops you right as you're about to pull yourself out, rushing off into the distance to grab something. When he appears again, he's holding his helmet for the weekend and donning a mischievous smile.

"You have to try it on," he laughs - and you're so enamoured by the sound of Oscar Piastri laughing that you have no choice outside of obliging. Obediently, you sit in place as he pushes the helmet down onto your head, and you let out a soft grunt at the feeling.

"How do you feel?" he asks.

"Squashed," you reply, voice muffled by the helmet.

"Oh, hold on," he lets out a soft laugh as he reaches towards you, flipping up the visor, "there you are."

"Thanks," you let out, but he doesn't lean back, instead leaning in even closer to the point where his nose almost touches the helmet.

"You have the prettiest eyes I've ever seen," he breathes in awe, just above a whisper. You feel your eyes widen, and you feel slightly grateful for the fact that the helmet covers up most of your face - which you're sure is bright red by now.

"Wh- sorry?" is all you can muster out as your boyfriend straightens back up with a smirk at your reaction, already whipping out his phone to snap a photo of you. "Hey!"

"You're so cute," he laughs, "this one's going in the race weekend photo dump for sure."