AWS Data Analytics Services

AWS data analytics has gotten complicated with all the services Amazon keeps launching. As someone who has built data pipelines across Redshift, Athena, Kinesis, and the rest of the AWS analytics stack, I learned everything there is to know about when to use each one. Today, I will share it all with you.

The AWS analytics portfolio covers everything from data warehousing to real-time streaming to business intelligence. Each service fills a specific niche, and understanding where they fit saves you from overengineering simple problems or underengineering complex ones.

Amazon Redshift

Probably should have led with this section, honestly. Redshift is the workhorse of AWS analytics—the fully managed data warehouse that handles everything from a few hundred gigabytes to petabytes without breaking a sweat.

The columnar storage technology is what makes Redshift fast. Traditional databases store data row by row, which works great for transactional operations but struggles with analytics. When you’re summing up revenue across millions of records, you only need the revenue column—not the customer name, address, or order date. Columnar storage lets Redshift read just what it needs and skip everything else.

Compression works better on columns too. A column of product categories contains repeated values that compress well. A column of prices follows predictable patterns. This compression reduces storage costs and speeds up queries because there’s less data to move around.

The parallel processing architecture distributes your data and queries across multiple compute nodes. When you run a query, each node handles its slice of the data independently, then combines results. This is why Redshift can query petabytes while your laptop database chokes on a few million rows.

What I actually use it for: loading data from S3 and DynamoDB, then running business intelligence queries. The COPY command from S3 is remarkably efficient—Redshift parallelizes the load across all nodes automatically. You can have terabytes of data flowing in while queries continue running on existing data.

The integration with other AWS services is where Redshift shines. You can query data in S3 directly using Redshift Spectrum without loading it into the warehouse first. You can federate queries across RDS and Aurora. You can export results back to S3 for other services to consume.

Amazon Athena

That’s what makes Athena endearing to us data folks—no clusters to spin up, no capacity planning, no maintenance windows. It’s serverless SQL directly on S3. Your data sits in buckets, you write a query, and Athena figures out the rest.

The pricing model is refreshingly simple: you pay per terabyte of data scanned. No running costs when you’re not querying. No minimum fees. This makes Athena perfect for ad-hoc analysis where you might run a dozen queries one week and none the next.

Athena supports the formats you’d expect: CSV, JSON, ORC, Parquet, and a few others. The format choice matters more than you might think. With CSV, Athena has to scan every byte of every file to answer any question. With Parquet or ORC, it can skip directly to the columns it needs. A query that scans 1TB of CSV might scan 50GB of the same data in Parquet—and cost 95% less.

The AWS Glue Data Catalog handles metadata. Glue crawlers inspect your S3 buckets, infer the schema, and create table definitions that Athena can query. You can also define tables manually if the crawler doesn’t get the schema right, which happens with unusual file formats or nested JSON.

SQL analysts love Athena because it speaks standard SQL. You can join tables, aggregate data, use window functions—everything you’d do in a traditional database. The only difference is your tables live in S3 instead of a database server.

I reach for Athena when someone needs a quick answer from historical data. No waiting for data loads, no cluster provisioning, no cleanup afterward. Point, click, query, pay for what you used.

Amazon Kinesis

When data needs processing as it arrives—not minutes or hours later—Kinesis handles the job. Real-time streaming for clickstreams, IoT sensors, application logs, financial transactions, any data that flows continuously.

Kinesis breaks into three main pieces, each handling a different part of the streaming puzzle:

Kinesis Data Streams is the foundation. Data producers send records to a stream, and consumers read them out. You control the shard count (which determines throughput capacity) and retention period (how long records stay available). Think of it as a highly durable message bus that can handle millions of records per second.

Kinesis Data Firehose eliminates the consumer code entirely for common destinations. Point Firehose at a stream, tell it where you want data to land—S3, Redshift, OpenSearch, Splunk—and it handles batching, compression, encryption, and delivery automatically. Most of my log aggregation pipelines use Firehose because there’s literally no code to write or maintain.

Kinesis Data Analytics runs SQL on streaming data. Real-time aggregations, windowed computations, anomaly detection—the kinds of analysis that used to require custom Spark Streaming jobs now work with familiar SQL syntax. Useful for dashboards that need to reflect data within seconds of arrival.

The pricing follows the pay-for-what-you-use model. Data Streams charges by shard-hour and PUT operations. Firehose charges by data volume ingested. Data Analytics charges by Kinesis Processing Unit hour. None of it is cheap at scale, but it’s cheaper than running your own Kafka cluster with the operational overhead that entails.

I’ve deployed Kinesis for application log aggregation more than any other use case. The pattern is simple: applications write logs to a Kinesis stream, Firehose batches them into S3 in five-minute windows, Glue catalogs the data, and Athena makes it queryable. The whole pipeline runs hands-off once configured.

AWS Glue

Glue is the ETL service—extract, transform, load. It discovers data, catalogs metadata, and runs transformation jobs. Think of it as the plumbing that connects your data sources to your analytics destinations.

The Data Catalog is Glue’s most useful piece. It stores metadata about your datasets—schemas, partitions, table definitions, statistics—and makes that information available across AWS services. Athena uses it for table definitions. Redshift Spectrum queries through it. EMR reads from it. One catalog, many consumers.

Crawlers populate the catalog automatically. Point a crawler at an S3 path, and it samples the files, infers the schema, and creates or updates table definitions. Crawlers handle schema evolution too—if new columns appear in your data, the crawler notices and updates the catalog.

For ETL jobs, Glue generates PySpark code that you can customize. The visual interface handles simple transformations: field mapping, filtering, joining sources. Complex logic still requires editing the generated code or writing your own. Glue runs the jobs on managed Spark clusters that spin up on demand and terminate when the job completes.

Glue also offers a serverless option called Glue ETL that charges per DPU-hour (Data Processing Unit). You don’t manage clusters or capacity—just specify how many DPUs you want and let Glue figure out the infrastructure. Useful for variable workloads where you don’t want to pay for idle capacity.

The workflow feature chains jobs together with dependencies. Job A completes, then Job B starts. If Job A fails, Job B doesn’t run. Triggers can start workflows on schedules or in response to events. It’s not as sophisticated as a full workflow orchestrator like Airflow, but it handles straightforward pipelines without external tools.

Amazon EMR

EMR runs Apache Spark, Hadoop, Flink, Presto, and other open-source big data frameworks on managed clusters. If your workload requires these tools—and many large-scale data processing jobs do—EMR provides them without the operational burden of managing your own installation.

You specify what you want (instance types, number of nodes, applications to install), and EMR provisions a cluster configured and ready to use. When the job finishes, terminate the cluster and stop paying. Or keep a persistent cluster running for interactive workloads.

Spot instances drop costs dramatically. Big data jobs are often fault-tolerant—if a node dies, the framework reruns the failed tasks on other nodes. This makes Spot a natural fit. I’ve seen EMR costs drop 60-70% by running task nodes on Spot while keeping master nodes on On-Demand for stability.

EMR notebooks bring Jupyter-style interactive development to the platform. Data scientists can explore data, prototype transformations, and test models in notebooks, then productionize the code as scheduled EMR steps. The iteration speed beats writing scripts locally and uploading them to clusters.

The pricing stacks EC2 costs plus an EMR premium that varies by instance type. For compute-intensive work on large datasets, EMR often beats Glue on cost. For smaller jobs with sporadic schedules, Glue’s serverless model usually wins. The breakeven point depends on your specific workload patterns.

I reach for EMR when jobs exceed what Glue handles gracefully—complex multi-stage pipelines, custom Spark libraries, workloads that need fine-grained cluster tuning. If the job fits Glue’s model, I use Glue. If it doesn’t, EMR.

Amazon QuickSight

QuickSight is the business intelligence service. Connect it to your data sources—Redshift, Athena, S3, RDS, and many others—then build interactive dashboards that stakeholders can explore without writing SQL.

The SPICE engine sets QuickSight apart from traditional BI tools that query source systems directly. SPICE is an in-memory cache that holds your data locally within QuickSight. When users interact with dashboards, queries hit SPICE instead of hammering your production databases. The result: fast, responsive dashboards that don’t impact source system performance.

You can import data into SPICE manually or schedule refreshes. The cache updates in the background, and dashboards reflect the refreshed data automatically. For real-time needs, direct query mode skips SPICE and queries the source system live, though this trades responsiveness for freshness.

Dashboard creation uses a drag-and-drop interface. Drag fields onto visualizations, apply filters, create calculated fields—the standard BI toolkit. QuickSight supports the usual chart types: bar, line, scatter, pie, maps, tables, and more specialized options like sankey diagrams and word clouds.

The pricing model splits into authors and readers. Authors create and edit dashboards; readers view and interact with them. Reader pricing is particularly attractive—charges cap per reader per month regardless of how much they use the dashboards. This makes QuickSight affordable for broad organizational distribution.

QuickSight also offers ML-powered insights. Point it at a dataset and it automatically identifies anomalies, trends, and forecasts. The quality varies depending on your data, but the feature adds analytical capabilities that would otherwise require data science expertise.

Amazon OpenSearch Service

OpenSearch (previously Elasticsearch Service) handles full-text search, log analytics, and real-time application monitoring. If you need to search through unstructured text or analyze log data at scale, this is the service.

The architecture centers on clusters of nodes that index and search documents. You choose instance types and counts, OpenSearch handles replication and distribution. Scaling means adding nodes—OpenSearch redistributes data automatically.

For log analytics, Kinesis Firehose streams data directly into OpenSearch. Application logs arrive within seconds, indexed and searchable. OpenSearch Dashboards (formerly Kibana) provides the visualization layer: search interfaces, charts, dashboards, alerts.

The query language supports both simple keyword searches and complex boolean queries with aggregations. You can search for error messages by text, then aggregate by source IP, time bucket, and error type—all in one query. This flexibility makes OpenSearch the default choice for log analysis in many organizations.

UltraWarm and cold storage tiers reduce costs for older data. Recent logs stay on fast storage for active investigation. Older logs move to cheaper tiers where queries run slower but storage costs drop significantly. You set policies that migrate data automatically based on age.

Security integrates with AWS IAM for access control. Fine-grained permissions let you restrict who can see which indexes—useful when different teams share a cluster but need data isolation.

Amazon Rekognition

Rekognition brings machine learning to image and video analysis. Facial recognition, object detection, text extraction, content moderation—capabilities that would require significant ML expertise to build from scratch.

The service works through API calls. Send an image, get back labels identifying objects and scenes. Send a video, get timestamps where faces appear or specific actions occur. No ML models to train or infrastructure to manage.

Use cases span industries: security systems identifying faces, content platforms flagging inappropriate images, retail analyzing product placement, media companies cataloging video archives. The accuracy is good enough for most applications, though edge cases will always exist.

Rekognition fits into broader analytics pipelines when you need to analyze visual content. Store images in S3, process them with Rekognition, store the resulting metadata in DynamoDB or Redshift, query it alongside your other business data. The visual analysis becomes another data source feeding your analytics.

Picking the Right Service

Here’s the mental model I use when choosing services:

• Structured data warehouse queries on large datasets – Redshift
• Ad-hoc SQL on S3 data without infrastructure – Athena
• Real-time streaming and event processing – Kinesis
• ETL jobs and data catalog management – Glue
• Spark, Hadoop, or Flink workloads – EMR
• Business dashboards and reporting – QuickSight
• Log search and application monitoring – OpenSearch
• Image and video analysis – Rekognition

Real architectures combine several of these. A common pattern: Kinesis ingests streaming data, Firehose loads it to S3, Glue catalogs it, Athena handles ad-hoc queries, Redshift serves the BI workload, and QuickSight puts dashboards in front of business users. Each service handles what it does best.

Start with IAM permissions locked down. Data security is far easier to build in from the start than retrofit after you’ve got data flowing through multiple services. Define least-privilege roles for each component, and audit access regularly.

The AWS analytics stack keeps growing. New services launch, existing services gain features, and the boundaries between them blur. But the fundamentals stay constant: understand your data patterns, match services to requirements, and build pipelines that can evolve as needs change.

Cost Considerations

AWS analytics costs can surprise you if you’re not paying attention. Each service has its own pricing model, and the meter runs differently depending on how you use them.

Redshift charges by node-hour. Bigger nodes cost more, but you’re paying whether you’re running queries or not. Reserved instances cut costs substantially if you commit to running clusters long-term. The newer Redshift Serverless option charges by compute time, which suits variable workloads better than provisioned clusters.

Athena charges by data scanned. Partitioning your data and using columnar formats like Parquet can reduce costs by 90% or more. The query charges add up fast on large datasets—I’ve seen bills where a single poorly-optimized query cost more than a month of Redshift running.

Kinesis pricing gets complicated with shards, PUT operations, and extended retention. For simple log aggregation, Firehose is often cheaper because it handles batching efficiently. For real-time applications that need sub-second latency, Data Streams is worth the premium.

Glue has both job pricing and crawler pricing. The crawler costs catch some teams off guard—running crawlers frequently on large buckets adds up. Consider triggering crawlers on schedule rather than continuously for cost control.

EMR is EC2 pricing plus an EMR premium. Spot instances for task nodes dramatically reduce costs for fault-tolerant workloads. The managed scaling feature helps match cluster size to workload, avoiding paying for idle capacity.

OpenSearch clusters run continuously, so rightsizing matters. UltraWarm storage for older data costs a fraction of hot storage. Index lifecycle policies that age data automatically prevent storage costs from growing indefinitely.

QuickSight’s per-user pricing is straightforward but watch the SPICE capacity. You get some SPICE storage included, but exceeding it incurs additional charges that grow with your data volume.

Getting Help

AWS documentation covers each service thoroughly, though the sheer volume can be overwhelming. The architecture blog and “This Is My Architecture” video series show real implementations that help translate theory into practice.

AWS Training offers free digital courses and paid instructor-led training for deeper learning. The Data Analytics specialty certification validates practical knowledge across the analytics portfolio—studying for it is actually a good way to learn the services systematically.

For complex implementations, AWS Professional Services and the partner network bring hands-on expertise. Sometimes paying for experience beats struggling through trial and error, especially when the data pipeline needs to work correctly the first time.