10 AWS Analytics Services: Tools, Benefits, and Best Use Cases

Executive Summary

• You already know that the challenge is turning data into decisions that matter. So, the top AWS analytics services in this article will show you how to do that with clarity and control. Here, we will:

  •     Compare Redshift, Athena, Glue, and Kinesis for performance, cost, and scalability.
  •     See how Nova Cloud integrates data lakes and FinOps practices to streamline operations.
  •     Learn which service combinations deliver faster analytics and cleaner governance.

Ready to simplify your stack? Book a call with Nova Cloud to build your AWS analytics roadmap.

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Your teams might already use Amazon Web Services for analytics. But choosing the right mix of tools still feels like a constant trade-off between cost, performance, and control.

Each service promises speed and scale, yet not all deliver equal value once your data volume grows. So, you probably want clarity on which options actually fit your architecture and your goals.

In this article, you’ll see how the top 10 AWS analytics services compare and which combination works best for you. We'll cover everything you need to know, so let's get started!

What Is AWS Analytics?

AWS analytics is a collection of services that handle data ingestion, storage, querying, machine learning, and visualization (all within a unified cloud environment). It lets you scale workloads on demand and replace rigid on-prem systems that slow down insight delivery.

To see how AWS unifies data lakes, warehouses, and analytics services in action, watch this quick AWS overview:

And since the global high-performance data analytics market is projected to reach competitive pricing.87 billion by 2030, your competitors are probably already taking advantage of these services. $351.87 billion by 2030

That brings us to the next point:

Why AWS Analytics Matters for Enterprises

AWS analytics matters because it helps you make faster decisions using scalable, cloud-based infrastructure that lowers your total cost of ownership. Traditional setups struggle to keep up with rising data volumes, compliance needs, and disconnected tools that slow down insight delivery.

With AWS, you unify your data warehouses, pipelines, and business intelligence systems into one ecosystem that grows with your business. And that’s where data starts driving results.

Pro tip: Wondering what moving to AWS actually does for retailers beyond shifting workloads? Explore the biggest advantages others are using to drive growth and agility.

But, you need the right partner to design a setup that actually works.

How We Selected the Top 10 AWS Analytics Services

Choosing the right data analytics tools on AWS starts with identifying which ones deliver measurable results in real workloads. The services in this list were picked based on how well they solve data challenges that actually matter to enterprise teams.

Here are the criteria that shaped our selection:

•     Breadth of enterprise adoption.
•     Maturity and support ecosystem.
•     Relevance to key analytics use cases in finance, operations, retail, and SaaS.
•     Integration strength across data lakes, data Integration, and Amazon machine learning pipelines.

Each service meets these benchmarks with proven scalability and architectural fit.

Top 10 AWS Analytics Services

Before you look at individual AWS tools, start with Nova Cloud, the consulting partner that helps you connect and optimize them. Nova Cloud builds modern analytics frameworks using services like Athena, Redshift, Glue, and Kinesis.

Together, they help you streamline data processing, control costs, and deliver insights faster. Here are the 10 services and solutions that matter most right now.

1. Nova Cloud (AWS Analytics Consulting & FinOps Partner)

Nova Cloud is an AWS Advanced Tier Services Partner that helps you design, integrate, and optimize complete analytics stacks built on AWS. Instead of offering a standalone analytics product, Nova Cloud builds real-time streaming and data pipeline architectures for your business needs.

And our clients always get good results.

Take UpHome, a 3D bathroom design site. When it started running into limits with its single-server setup, Nova Cloud rebuilt its entire platform on AWS. The new cloud environment uses EC2 Auto Scaling, Elastic Load Balancing, and CloudFormation. All this keeps performance consistent even under pressure.

In fact, after migration, the site handled 20,000+ concurrent users without slowing down. Now, it runs faster, safer, and with daily automated backups. Plus, this flexible, high-availability setup saves costs, is reliable, and gives users a better design experience.

Here’s how we do it.

Our engineers use AWS Glue, Redshift, and Athena to build modern data lakes and interactive dashboards that turn raw data into decisions. In retail and CPG, Nova Cloud delivers real-time logistics, demand forecasting, and inventory visibility using Kinesis, IoT, and SageMaker.

You also get FinOps consulting that rightsizes workloads, controls spend, and improves reporting visibility across teams. Nova Cloud combines deep AWS knowledge with business fluency so your data infrastructure runs efficiently, scales cleanly, and supports measurable business outcomes.

Pros:

•     Advanced AWS certifications and delivery programs.
•     Proven success in retail and CPG analytics systems.
•     Full lifecycle support, from design to optimization and FinOps.

Cons:

•     No proprietary analytics engine.
•     AWS-focused, not multi-cloud.

Pricing: Custom quote.

Pro tip: Battling to make your retail or CPG data stack run smoothly in the cloud? Find out which specialist firms deliver real cost and performance gains.

2. Amazon Athena

Amazon Athena is a serverless query engine that lets you run SQL queries directly on data stored in Amazon S3 without managing servers or clusters. It supports open formats like Parquet, ORC, and JSON, and connects to the AWS Glue Data Catalog for schema discovery and metadata.

This flexibility helps you analyze structured and semi-structured data quickly while paying only for the data you scan. For example, FINRA uses Athena to analyze over 150 petabytes of regulatory data stored in Amazon S3. It’s how they detect fraud faster and cut query times to minutes.

Nova Cloud helps you design data lakes, optimize query performance, and integrate Athena into broader analytics stacks using Glue, Redshift, and QuickSight.

Pros:

•     Serverless, no infrastructure to manage.
•     Integrates with Glue and QuickSight for analytics workflows.
•     Supports flexible file formats for large-scale data analysis.

Cons:

•     Read-only (no data manipulation).
•     Query costs rise with poor partitioning.
•     Performance varies for complex joins.

Pricing: Starts at $5 per terabyte of data scanned.

3. Amazon Redshift

Amazon Redshift is AWS’s fully managed data warehousing service built for high-performance SQL analytics across petabyte-scale datasets. It uses a massively parallel processing architecture and columnar storage for faster queries and lower latency.

And it’s a very useful service. Peloton scaled analytics for higher data growth using Amazon Redshift, and cut query delays with Concurrency Scaling and Serverless. This helped teams gain real-time business insights while lowering infrastructure costs.

Besides, Redshift integrates directly with Amazon S3 and the AWS ecosystem, so you can query data where it lives without moving it.

Nova Cloud helps you design, tune, and modernize your Redshift environment. Our team can help you do this by implementing right-sized clusters, cost governance, and zero-ETL pipelines that align with your data goals.

Pros:

•     Handles large workloads with parallel query execution.
•     Integrates tightly with S3, Glue, and QuickSight.
•     Flexible pricing models for compute and storage.

Cons:

•     Requires tuning for peak performance.
•     No built-in uniqueness constraints.

Pricing: Starts around no additional cost.25 per node hour.

4. Amazon QuickSight

Amazon QuickSight is AWS’s fully managed business intelligence service for creating dashboards, reports, and visual analytics at scale. It uses the SPICE engine (Super-fast, Parallel, In-memory Calculation Engine) to accelerate queries and supports integration with Amazon S3, Redshift, Athena, and other third-party data sources.

QuickSight includes AI-powered analytics like anomaly detection, forecasting, and natural language queries through QuickSight Q. The platform is serverless, automatically scaling to thousands of users without infrastructure management.

For example, Docebo used QuickSight to embed analytics into its platform. The company cut implementation time by 60% and increased adoption fivefold. This helped their customers access customized dashboards with secure, role-based access.

Pros:

•     Scales automatically and embeds easily into applications.
•     Integrates natively with key AWS data services.
•     Uses built-in ML and natural language for faster insight.

Cons:

•     Limited layout customization.
•     Live queries can slow down on large datasets.
•     Complex ETL requires Glue or third-party tools.

Pricing: Starts at affordable pricing per reader per month.

5. AWS Glue

AWS Glue is a fully managed, serverless service that simplifies data management across analytics pipelines. It automates the discovery, cataloging, and transformation of data from multiple sources.

With the AWS Glue Data Catalog, you can organize and share metadata across services like Redshift, Athena, and Lake Formation. Glue supports both batch and real time data movement, which allows you to build scalable ETL workflows with minimal setup.

Many companies have good results with it. Stifel, for instance, used AWS Glue to modernize its financial data platform. The company then cut manual processing, improved governance, and enabled near real-time insights across business units.

Nova Cloud helps you implement Glue for automated pipelines, schema management, and cross-domain data integration. This can help you optimize jobs for speed and cost.

Pros:

•     Serverless, scales automatically.
•     Auto-generates ETL code in Python or Scala.
•     Integrated catalog for cross-service metadata.

Cons:

•     Requires Spark or Python knowledge.
•     AWS-centric integration.
•     Limited multi-language flexibility.

Pricing: Starts at no additional cost.29 per DPU hour.

Ready to make your AWS analytics actually work for you? Talk to Nova Cloud and start building a data stack that drives real results.

6. Amazon EMR

Amazon EMR (Elastic MapReduce) is AWS’s managed Hadoop framework for large-scale data processing. It runs open-source tools like Spark, Hive, Presto, and Flink on scalable EC2 clusters or through EMR Serverless.

With it, you can process data-driven problems across petabyte-scale datasets without managing infrastructure manually. EMR integrates with cloud storage like Amazon S3 and supports managed scaling, auto termination, and EMR Studio for interactive development.

One good example is Zillow Group, which used EMR with Spark and Kinesis to power real-time home valuations. The company cut model computation from a full day to just hours. This led to faster pricing updates and improved market response time.

Pros:

•     Pay-per-use model with flexible instance types.
•     Scales clusters quickly with managed scaling.
•     Supports multiple open-source frameworks and AWS integrations.

Cons:

•     Requires tuning for high-performance workloads.
•     Limited built-in observability tools.

Pricing: Starts at per-second billing for EC2 instances used.

7. Amazon Kinesis

Amazon Kinesis gives you a full suite of tools for processing real-time data streams at scale. You can capture, process, and analyze continuous data from IoT devices, app logs, or clickstreams using Amazon Kinesis Data Streams.

In fact, Wyze used Kinesis Video Streams to handle video from thousands of smart home cameras. As a result, they get live playback and AI-based motion detection with minimal latency.

Meanwhile, Kinesis Data Firehose automatically delivers it to S3, Redshift, or OpenSearch.

For deeper insights, Kinesis Data Analytics supports SQL or Apache Flink for near-instant analysis. Also, Nova Cloud helps you build streaming pipelines that merge Kinesis with Redshift and Glue. This ensures efficient, low-latency data integration across your architecture.

Pros:

•     Real-time data capture and processing.
•     Scales automatically to handle variable workloads.
•     Integrates with key AWS analytics and storage tools.

Cons:

•     Retention limits for streams.
•     Complex setup for hybrid data sources.
•     AWS API dependency.

Pricing: Starts with pay-per-GB ingestion and per-hour shard billing.

8. AWS Data Pipeline

AWS Data Pipeline automates how you move and process data across AWS compute and storage services. It schedules, retries, and monitors tasks like copying data between S3 and Redshift, running SQL queries, or triggering EMR jobs.

Pipelines are defined in JSON, and they let you control dependencies, error handling, and task conditions.

Nova Cloud helps you modernize legacy pipelines by migrating workflows to AWS Glue or Step Functions for better scalability, visibility, and security compliance. This can reduce your maintenance overhead while improving automation reliability.

Pros:

•     Simple setup with prebuilt templates and visual scheduling.
•     Cost-efficient execution (resources run only when needed).
•     Secure and fault-tolerant with encrypted data and automatic retries.

Cons:

•     Complex branching logic for advanced workflows.
•     Manual management of multiple pipelines over time.

Pricing: Same as AWS Glue.

Pro tip: Planning a move to AWS but worried about risks to your retail or CPG operations? See which partners are trusted to guide complex migrations and unlock new value.

9. AWS Lake Formation

You can use AWS Lake Formation to build and manage secure data lakes on Amazon S3 with automated ingestion, cataloging, and fine-grained access control. It simplifies how you set permissions, classify data, and integrate analytics tools like Athena, Redshift Spectrum, and QuickSight under one governance model.

In fact, JPMorgan Chase used Lake Formation to build a governed data mesh across business units. The result was faster cross-domain data sharing without compromising security or compliance.

Not sure how to start?

Nova Cloud helps you deploy Lake Formation efficiently by aligning its governance model with your existing AWS stack. It also streamlines setup through proven data architecture practices and automation for faster, more consistent implementation.

Pros:

•     Automates data ingestion, cleansing, and classification.
•     Enables granular, auditable access policies.
•     Integrates seamlessly with AWS analytics tools.

Cons:

•     Requires learning Lake Formation’s permissions model.
•     Limited to data stored in S3.
•     Metadata transactions may incur extra charges.

Pricing: Permission features are free.

10. Amazon OpenSearch Service

Amazon OpenSearch Service is a managed search and analytics platform built for large-scale log analysis, observability, and full-text search. It supports structured and unstructured data and can run on either provisioned clusters or serverless infrastructure.

You can use it to analyze application logs, power search-driven applications, or build real-time monitoring dashboards across your AWS environment. For example, Compass used OpenSearch to deliver faster, data-rich search results across millions of listings. This helped them cut latency while improving reliability.

Pros:

•     Simplifies deployment, scaling, and monitoring.
•     Natively integrates with AWS services like Kinesis and CloudWatch.
•     The serverless option removes provisioning overhead.

Cons:

•     Limited control and plugin support.
•     Pricing can rise with scale and complex workloads.
•     Upgrades and maintenance depend on AWS release cycles.

Pricing: A free tier is available.

Pro tip: Need a better way to watch your eCommerce apps and catch issues early? See which agencies excel at blending observability tools and app health insights.

How to Choose the Right AWS Analytics Service

Choosing the right AWS analytics tool depends on how your teams use data day to day. The right fit helps you balance performance, scalability, and visibility without overengineering the stack.

Here are the main factors to guide your decision:

•     Map each service to its best-fit workload: Ad-hoc querying, real-time streaming, BI dashboards, or machine learning prep.
•     Weigh cost against performance: Athena might suit on-demand querying, while Redshift fits steady, high-volume workloads.
•     Assess integration needs with current data: This includes ERP, CRM, or IoT platforms to avoid unnecessary migration overhead.

The goal is to build a system that aligns with your data maturity, team structure, and long-term scalability. When done right, your analytics stack runs efficiently, adapts quickly, and supports real business decisions.

Common Challenges with AWS Analytics Deployments

Even experienced teams can run into friction when scaling analytics on AWS. Oftentimes, the challenges might be technical at first. But they can also show up in cost control, governance, and visibility.

These are the main issues you’ll likely face:

•     Hidden costs: Cross-region queries, unmanaged data egress, and inefficient query design can quickly erode ROI if not monitored closely.
•     Governance and security gaps: Complex permission models and decentralized access control typically make compliance harder to maintain across teams and regions.
•     Skill gaps: Managing multiple overlapping analytics services requires deep AWS expertise, and misconfigurations can affect both performance and accuracy.
•     Limited observability: Tracking data lineage, query performance, and pipeline health across services usually requires manual effort or third-party tooling.

Turn AWS Analytics into Business Advantage with Nova Cloud

Choosing the right AWS analytics stack is about control, visibility, and outcomes that actually move the business forward. Every service has trade-offs, and success depends on how well they’re connected, secured, and optimized for your workloads.

Well, that’s where Nova Cloud comes in. Our AWS-certified experts help you design, integrate, and manage analytics ecosystems that deliver faster insights, tighter governance, and measurable ROI.

Reach out to us to turn your AWS data strategy into a growth engine.

FAQ

Which AWS analytics service is best for real-time data?

Amazon Kinesis is best for real-time data. It ingests and processes streams from apps, IoT devices, and logs within seconds. This gives you immediate visibility into live operations.

What’s the difference between Redshift and Athena?

Redshift is a managed data warehouse built for high-volume, structured analytics. Meanwhile, Athena runs ad-hoc SQL queries directly on S3 data. Redshift suits consistent, heavy workloads, but Athena fits flexible, on-demand querying.

Does AWS have a BI tool?

Yes. Amazon QuickSight is AWS’s native BI service. It lets you build dashboards and reports directly from your AWS data. This comes complete with ML-powered insights and natural language queries.

How do I control costs in AWS analytics?

You can start with data partitioning and compression to minimize scan volume. Then use lifecycle policies, reserved pricing, and FinOps reviews to keep infrastructure aligned with actual usage.