Transition from AWS Big Data to Data Analytics Certification Explained

As the digital transformation wave continues to intensify, organizations are leveraging data at an unprecedented scale to derive actionable insights and drive innovation. Cloud computing, particularly through Amazon Web Services (AWS), has been central to this shift. In line with industry needs and its expanding service portfolio, AWS made a major change in its certification lineup: the retirement of the AWS Certified Big Data – Specialty certification and the launch of the AWS Certified Data Analytics – Specialty exam.

This change signifies more than a title update—it marks a strategic pivot in how AWS aligns its certifications with real-world cloud roles. While “Big Data” once symbolized cutting-edge data processing, the field has since matured. Data analytics today encompasses more than massive batch data workloads. It includes real-time processing, advanced visualization, automated pipelines, and security at scale. The rebranded certification reflects this evolution.

Certification Transition Timeline and Background

AWS formally introduced the AWS Certified Data Analytics – Specialty (DAS-C01) in 2020, following the retirement of the AWS Certified Big Data – Specialty (BDS-C00). The beta version of DAS-C01 concluded in early 2020, with the full exam officially available beginning April 13, 2020. This coincided with the extension of the old Big Data exam’s availability, which was initially set to end in April but was pushed to June 30, 2020.

This overlap allowed candidates who had been preparing for BDS-C00 to complete their certification path while enabling a smooth transition to the updated exam structure. This move was particularly relevant for professionals already on their learning journey, ensuring they weren’t caught off guard by the sudden shift.

Why AWS Retired the Big Data Specialty Exam

When AWS launched the Big Data Specialty certification, cloud-based analytics was still in its formative stage. Most enterprises were either just beginning to move their data to the cloud or experimenting with Hadoop-based clusters for batch processing. Over the years, AWS introduced a broader set of analytics tools such as Amazon Kinesis for real-time data streaming, QuickSight for business intelligence dashboards, and AWS Glue for serverless data transformation.

The term “Big Data” became too narrow to encompass the expanding use cases that modern data professionals handle on AWS. The name change to “Data Analytics” reflects a broader set of roles and responsibilities, including data engineers, architects, and analytics specialists who work across various stages of the data lifecycle—from collection to visualization.

More importantly, this change aligns AWS certifications with current job roles in the cloud industry. Organizations today seek professionals who can work with structured and unstructured data, real-time analytics, data lakes, dashboards, and automated pipelines—not just batch processing frameworks.

Certification Format and Consistent Mechanics

Despite the broader scope and increased expectations, the core structure of the certification exam remains familiar to those who’ve taken other AWS certifications. The DAS-C01 exam is multiple-choice and multiple-response. There is no penalty for incorrect answers—only unanswered questions are marked wrong, encouraging candidates to attempt every item.

The exam score ranges from 100 to 1,000, with a passing threshold of 750. Unlike some other certification programs, AWS does not require passing each domain individually. You only need to pass the overall exam. Some of the questions are unscored and are used to evaluate new question formats and topics for future exams.

This consistency ensures that existing AWS-certified professionals will find the DAS-C01 format approachable, even if the domain content has evolved.

Enhanced Experience Requirements

One of the biggest shifts introduced with the new certification is the experience level expected from candidates. Previously, the Big Data – Specialty exam recommended two years of hands-on experience working with AWS data services. With DAS-C01, AWS now suggests five years of experience with common data analytics technologies and at least two years of experience specifically using AWS analytics services.

This signals AWS’s intention to position this exam as an advanced-level certification. Candidates are expected to bring deep knowledge of data engineering and analytics concepts, familiarity with designing production-grade pipelines, and comfort in optimizing cost and performance within AWS environments. This increased bar ensures that those who achieve this certification truly reflect senior-level cloud analytics capability.

Scope of Services in the New Exam

The DAS-C01 certification covers a broad range of AWS services that play crucial roles across the data analytics lifecycle:

Amazon Kinesis: Real-time streaming ingestion and analytics
Amazon EMR: Managed big data processing with Apache Spark and Hadoop
Amazon Athena: Serverless SQL querying directly on S3 data
Amazon Redshift: A Scalable data warehouse for structured analytics
AWS Glue: Serverless ETL and metadata catalog
Amazon QuickSight: Business intelligence and interactive dashboards
AWS Lake Formation: Centralized data lake creation and governance
Amazon MSK (Managed Streaming for Apache Kafka): Event streaming for decoupled architectures
Amazon OpenSearch Service (formerly Elasticsearch Service): Operational analytics on log data and application telemetry

This diverse list underlines the need for cross-functional knowledge, not just around big data processing, but also around event-driven architecture, data governance, and business intelligence.

Changes to Exam Domains and Weightage

The domain structure of the certification exam has also been updated. While the Big Data – Specialty exam had more granular separation (including Visualization as a standalone domain), the new exam merges related topics and adjusts the weightage to reflect practical importance.

The Collection domain now includes evaluation of frequency, volume, and data source, emphasizing real-world ingestion patterns using services like Kinesis and Kafka. Storage has been expanded into “Storage and Data Management,” now including metadata, data cataloging, and lifecycle considerations. This domain has the second highest weight in the new exam, underscoring how data governance is becoming more critical in cloud environments.

Processing saw the biggest increase in weightage, jumping from 17% in the older exam to 24% in DAS-C01. It now includes automation, orchestration, and transformation workflows—areas where AWS Glue, Lambda, and EMR are commonly tested.

Meanwhile, Analysis and Visualization, previously two domains totaling 29%, are now merged into one with 18% weight. This reduction may surprise some, especially emphasized dashboarding in today’s data workflows. Still, key tools like QuickSight and Redshift remain essential knowledge areas.

Security, now at 18%, retains its importance but is refocused on encryption, IAM, and compliance rather than broader regulatory topics.

Why the Analysis and Visualization Weight Decreased

One of the more surprising aspects of the domain restructure is the drop in emphasis on analysis and visualization. In a world where data is increasingly interpreted through visual platforms and presented to business users, many professionals expect AWS to expand this area. However, it’s likely that AWS expects professionals to already understand BI tools conceptually, and instead wants to focus the exam on architecture, data movement, and system-level optimization.

That said, Amazon QuickSight, Redshift dashboards, and usage of visualization best practices are still relevant for several scenario-based questions. Candidates are encouraged to understand the different visualization capabilities, especially when working with multi-source data and complex transformations.

Certification Intent and Target Audience

The AWS Certified Data Analytics – Specialty certification is clearly targeted at mid to senior-level cloud professionals. These include:

Data engineers are designing and maintaining data pipelines
Analytics architects are building data lakes and warehousing solutions.
Developers working with real-time data streams and ETL processes
BI professionals are integrating data visualization with backend storage

AWS has shifted its focus from individuals who merely use tools to those who design and optimize the systems behind those tools. This aligns with broader trends in enterprise cloud hiring, where analytics infrastructure roles are becoming more strategic.

AWS’s retirement of the Big Data – Specialty certification and introduction of the Data Analytics – Specialty exam is a clear signal: analytics in the cloud is no longer a niche area—it is mainstream, strategic, and foundational. The new certification better represents the technologies, tools, and challenges that today’s data professionals face.

This is not a cosmetic change; it reflects a new maturity in the AWS analytics ecosystem. With updated domains, broader service coverage, and deeper expectations around architectural thinking, DAS-C01 sets a new bar for cloud data expertise.

Mastering Data Collection for AWS Data Analytics Certification

As the first step in the data lifecycle, data collection plays a foundational role in any cloud-based analytics solution. For professionals aiming to pass the AWS Certified Data Analytics – Specialty exam, Domain 1—Collection tests your understanding of ingesting data at scale, securely, and with the appropriate technology fit for different types of workloads.

In this series, we will examine how AWS enables data ingestion through purpose-built services, how to design architectures that support varying data volumes and formats, and what candidates need to focus on to perform well in the Collection domain.

Understanding the Role of Data Collection

In today’s data ecosystems, businesses collect data from an ever-growing list of sources: mobile applications, IoT devices, streaming APIs, SaaS platforms, clickstream logs, and transactional systems. These data streams may arrive in real-time, batches, or even irregular intervals. The Collection domain of the DAS-C01 certification evaluates your ability to design ingestion systems that can reliably, securely, and efficiently handle this complexity.

AWS offers a rich portfolio of services tailored to support a wide variety of ingestion use cases, each with its operational characteristics, scalability limits, and integration capabilities.

Key Data Ingestion Services on AWS

Let’s explore the major AWS services that you are expected to know in the Collection domain:

Amazon Kinesis

Amazon Kinesis is a family of services designed to handle real-time streaming data. Within this suite, Kinesis Data Streams allows you to ingest large volumes of time-ordered data in near real-time. It is frequently used to collect application logs, telemetry data, and streaming events from distributed systems.

You are expected to understand the differences between Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics:

Kinesis Data Streams requires manual configuration of shards and consumers and provides fine-tuned control over processing and scaling.
Kinesis Data Firehose is fully managed and automatically delivers data to destinations such as S3, Redshift, or OpenSearch without the need to manage consumers or write processing logic.
Kinesis Data Analytics can run SQL queries against streaming data, offering real-time transformation and filtering.

Candidates must know when to use each Kinesis component, how to configure data retention, and how to handle replay scenarios.

Amazon MSK (Managed Streaming for Apache Kafka)

Amazon MSK offers a managed version of Apache Kafka, a powerful distributed streaming platform used in many large-scale data ingestion pipelines. MSK provides compatibility with open-source Kafka APIs, allowing teams to migrate their existing workloads without refactoring.

Understanding when to choose MSK over Kinesis is key. While Kinesis is tightly integrated with AWS services, MSK is a better fit when you need Kafka’s ecosystem capabilities, such as Kafka Connect, or if your organization already uses Kafka in its on-premises systems.

AWS IoT Core

While less emphasized than in the older Big Data certification, AWS IoT Core may still appear in questions related to ingesting telemetry from connected devices. This is especially relevant for use cases in manufacturing, logistics, and agriculture.

You should understand how IoT rules can route messages to S3, DynamoDB, or Lambda functions, and how MQTT and HTTP protocols affect ingestion latency and security.

AWS DataSync

Although not explicitly part of every ingestion pipeline, AWS DataSync is often used in hybrid cloud scenarios where on-premises file systems must be synchronized with AWS S3 or EFS. Candidates may encounter scenarios where DataSync is the most appropriate choice for bulk transfers of structured and unstructured data.

Amazon S3 and Direct Uploads

Not all ingestion requires a stream or pipeline. For many batch workloads, simply uploading data to Amazon S3 through APIs, SDKs, or CLI tools is sufficient. Understanding multipart upload, server-side encryption, and S3 events for triggering processing is essential. These techniques often serve as the entry point to serverless ETL pipelines using services like AWS Lambda or AWS Glue.

Operational Characteristics You Must Know

The Collection domain goes beyond identifying services—it requires you to evaluate operational aspects such as:

Latency: Can the ingestion system deliver near real-time performance, or is eventual consistency acceptable?
Data order and structure: Does the pipeline need to preserve the order of messages (e.g., time-series logs)? Will the data arrive in JSON, Avro, Parquet, or another format?
Volume and throughput: Can the service scale to handle spikes in data input? How is throughput controlled or partitioned (as in the case of shards or partitions)?
Durability and reliability: What happens if the data stream is interrupted or throttled? Can you replay missed events?
Compression and encryption: Is the data compressed before ingestion to reduce storage and transfer costs? What encryption mechanisms apply at rest and in transit?

For example, a question may present a use case requiring ingestion of 5 TB of sensor data daily with millisecond latency. You’ll be expected to choose between Kinesis, MSK, or an S3-based ingestion, and justify your decision based on the system’s requirements.

Data Properties in Ingestion Design

Candidates should understand the characteristics of the data being collected. These include:

Frequency: High-frequency streams (e.g., telemetry every few seconds) versus low-frequency batch uploads (e.g., daily reports).
Format: Structured (CSV, JSON), semi-structured (Avro, ORC), or unstructured (images, PDFs).
Source diversity: Are you collecting data from web apps, sensors, mobile devices, or legacy systems? Each source may dictate the protocol, latency expectations, and authentication methods.

Compression and format are also important. Services like Kinesis Firehose allow compression using GZIP or Snappy before storing in S3. Choosing the right format affects downstream processing speed and cost.

Real-World Scenario Considerations

Here are examples of exam-style scenarios you may encounter in the Collection domain:

A logistics company collects geolocation data every second from 10,000 delivery vehicles. The solution must process and store data with minimal latency and allow downstream real-time analytics.
Ideal AWS services: Amazon Kinesis Data Streams or Amazon MSK, possibly coupled with Lambda or Kinesis Analytics.
A financial analytics team uploads CSV files nightly to a central location for batch ETL processing.
Ideal AWS service: Direct uploads to Amazon S3, possibly with S3 event notifications triggering AWS Glue jobs.
An agricultural company streams data from IoT devices across multiple farms.
Ideal AWS services: AWS IoT Core for ingestion, rules engine to route to S3 or Kinesis, with integration to Glue for processing.

These scenarios test your understanding of how ingestion architecture varies across use cases and how each AWS service aligns with those needs.

Tips for Exam Preparation

Here’s how to prepare effectively for the Collection domain of the AWS Certified Data Analytics – Specialty exam:

Get hands-on experience with Kinesis, MSK, and S3 ingestion workflows. AWS Free Tier provides limited but useful practice environments.
Review service limits and throughput patterns for Amazon Kinesis and MSK. Know how shards and partitions influence scalability.
Understand security best practices for data in transit and at rest, including the use of AWS KMS and IAM roles for authentication.
Study AWS whitepapers and FAQs for each ingestion service. They often explain subtle differences that can appear in exam questions.
Practice scenario-based questions that require selecting a service based on performance, security, or data structure requirements.

Mastering data collection is the cornerstone of effective analytics on AWS. Whether streaming logs from millions of mobile users or ingesting sensor data from remote devices, choosing the right collection strategy sets the stage for performance, cost-efficiency, and scalability downstream.

AWS’s emphasis on purpose-built ingestion services means there’s rarely a one-size-fits-all solution. Understanding each tool’s trade-offs and operational behavior is critical, not only for the exam but also for building real-world cloud-native data architectures.

Navigating Storage and Data Management for AWS Data Analytics Certification

In any data analytics pipeline, effective data storage and metadata management are fundamental for ensuring that insights can be generated efficiently, securely, and at scale. In Domain 2 of the AWS Certified Data Analytics – Specialty exam, candidates are tested on how well they can architect, manage, and optimize storage systems tailored to analytical workloads.

This domain emphasizes both the physical storage of data and the logical structures used to manage and retrieve that data. It’s no longer enough to simply land data in a data lake—you must also understand data layout, schema management, access patterns, and lifecycle policies that optimize costs and performance across diverse use cases.

Let’s explore the AWS services, patterns, and best practices that are critical for mastering this section of the certification.

The Evolving Role of Storage in Data Analytics

Traditional data systems focused on structured databases and well-defined schemas. Today’s data platforms deal with structured, semi-structured, and unstructured data across multiple storage layers and formats. With the explosion of data volume, modern analytics relies heavily on cloud-native storage technologies that provide:

High durability and availability
Low-latency retrieval
Scalability without manual provisioning
Secure access and fine-grained control
Integration with metadata catalogs

AWS has embraced this shift by offering a wide range of services built for analytical storage needs, including object storage, columnar data warehouses, metadata cataloging, and purpose-built tools for format conversion.

Core Storage Services for the DAS-C01 Exam

Amazon S3

Amazon Simple Storage Service (S3) is the backbone of most data lakes on AWS. Its flexibility, cost-efficiency, and native integration with many analytics services make it the primary data store for raw, curated, and transformed data.

For the exam, you’ll need to understand:

Storage classes: Standard, Intelligent-Tiering, Glacier, Glacier Deep Archive
Versioning and lifecycle rules to transition data between classes
Encryption options include SSE-S3, SSE-KMS, and client-side encryption.
Event notifications and integration with Lambda, Glue, and other services

S3 also supports multipart uploads for large datasets and automatic replication across regions when configured. You should understand how S3 performance is influenced by access patterns and how best to organize buckets and prefixes for analytics workloads.

AWS Glue Data Catalog

The AWS Glue Data Catalog acts as a centralized metadata repository that stores schema information, table definitions, and partitioning details for datasets stored in S3 and elsewhere. Many services—Athena, EMR, Redshift Spectrum—depend on the catalog to query data efficiently.

Key topics for the exam include:

Crawlers and how they scan and catalog new data
Schema evolution handling in Glue
Integration with Athena, Redshift Spectrum, and Lake Formation
Access control using resource-level IAM policies and Lake Formation permissions

Candidates should understand the importance of metadata and how Glue enables a schema-on-read approach that’s essential for querying semi-structured data.

Amazon Redshift

Redshift is a petabyte-scale, columnar data warehouse designed for high-performance queries on structured data. While it serves a different purpose than S3, Redshift often works alongside it using features like Redshift Spectrum.

Important features to study:

Table design and distribution styles (key, even, all)
Sort keys and compression (column encoding)
Workload management (WLM) for query performance tuning
Spectrum: How Redshift can query external data in S3 via the Glue Data Catalog

You should also be able to differentiate between scenarios that call for a fully-managed warehouse (Redshift) and scenarios that benefit from querying data directly in a data lake (Athena, EMR).

Data Management Practices and Lifecycle Strategies

Modern analytics workflows require not just raw storage but also governance and operational discipline. The DAS-C01 exam tests your knowledge of managing data throughout its lifecycle.

Data Layout and Formats

Efficient analytics begins with proper data layout. Candidates need to be familiar with:

Partitioning: breaking data into folders based on columns (e.g., date, region) to optimize query performance
File formats: Parquet, ORC, Avro, JSON, and CSV—know the pros and cons of each
Compression: how GZIP, Snappy, and LZO reduce storage size and improve performance

For example, columnar formats like Parquet and ORC are better suited for scan-heavy analytics queries in Redshift Spectrum or Athena, while JSON and CSV are easier to use for streaming or ingestion workflows.

Data Cataloging and Metadata Management

Metadata is the glue that connects storage systems with analytics engines. AWS Glue Crawlers and Catalog APIs allow automation of schema detection and table creation. You’re expected to know:

How to configure Glue Crawlers to scan S3 and detect schema changes
When to use manual table definitions for tighter control
How schema evolution is handled and how to prevent breaking changes
Query implications of missing or mismatched metadata

Metadata governance and tagging are also becoming increasingly important for data discovery and auditing across large organizations.

Lifecycle Policies and Cost Optimization

Efficient storage design is also about cost control. You should know how to:

Use S3 lifecycle policies to automatically transition data from Standard to Glacier or delete it
Analyze storage usage with S3 Storage Class Analysis.
Select data retention durations based on access patterns and compliance

For instance, older log files in S3 might be archived in Glacier Deep Archive after 90 days, while frequently accessed curated datasets stay in Intelligent-Tiering.

Scenario-Based Use Cases

The exam is likely to include scenario-based questions that test your ability to apply storage and data management principles in real-world settings. Here are a few examples of what you might encounter:

A media company stores video files in S3 and runs analytics on viewership logs daily
Best solution: Use S3 Standard for frequently accessed data and transition old logs using lifecycle policies. Optimize cost by storing large files in compressed formats.
An e-commerce platform needs to catalog daily sales data from multiple stores into a data lake.
Best solution: Use S3 to store CSV or Parquet files, AWS Glue Crawlers to update the Data Catalog daily, and Athena to run ad-hoc queries.
A research lab stores petabytes of scientific data, only analyzing it once a year
Best solution: Use S3 Glacier or Deep Archive for long-term storage, and apply lifecycle rules for automated tiering. Query with Redshift Spectrum or EMR on-demand.

Common Pitfalls to Avoid

When preparing for the exam or designing real-world solutions, beware of these common missteps:

Not partitioning large datasets in S3, leading to poor performance in Athena or Redshift Spectrum
Using JSON or CSV for a heavy analytical workload, where Parquet would be more efficient
.
Overlooking schema drift in Glue Catalogs, which can break downstream queries.
Failing to implement lifecycle policies and incurring unnecessary S3 storage costs
Choosing Redshift for use cases better suited for serverless querying via Athena

Tips for Studying This Domain

To succeed in Domain 2 of the AWS Certified Data Analytics – Specialty exam:

Get hands-on experience with S3, Glue, and Redshift using AWS Labs or free-tier projects
Understand how to set up lifecycle rules, create and manage Glue Crawlers, and optimize table layouts.
Practice writing SQL queries against partitioned and compressed datasets using Athena
Read AWS documentation and whitepapers on data lakes and best practices for data warehousing.
Experiment with Redshift Spectrum and understand how it queries external tables in S.3

Storage and data management are not just infrastructure concerns—they are central to performance, usability, and cost optimization in analytics pipelines. AWS provides a rich set of tools to manage structured and unstructured data, and mastering how to use them together is a key part of your journey toward certification.

Understanding how data is stored, cataloged, accessed, and archived across different AWS services is what separates a data engineer from a cloud architect. With this foundation, you’ll be well-equipped to handle complex analytics architectures and score high on this section of the exam.

Mastering Data Processing and Analysis for AWS Data Analytics Certification

In modern analytics architectures, raw data is rarely useful until it has been transformed, cleaned, and prepared for consumption. Data processing enables this transformation. Once the data is ready, it must be analyzed and visualized to produce business value. In the AWS Data Analytics – Specialty exam, these functions are represented by the domains on Processing (24%) and Analysis & Visualization (18%).

This series will walk through the key services, patterns, and real-world use cases you need to understand for these domains. Mastery of this content is not only crucial for passing the exam, but it also reflects your readiness to build scalable, efficient, and real-time analytics systems in the cloud.

Understanding Data Processing in the AWS Ecosystem

AWS provides several options for processing data at scale, each optimized for different workloads—whether real-time, batch, or near real-time. Candidates must demonstrate the ability to choose the right service, automate pipelines, and ensure efficient, fault-tolerant execution.

AWS Glue

Glue is a serverless data integration service designed to discover, prepare, and transform data for analytics. It’s heavily featured in the exam due to its central role in ETL workflows.

You should understand:

How to create Glue jobs using Python (PySpark) or Scala
Differences between Glue ETL and Glue Studio
Triggers and job scheduling for pipeline automation
Integration with Data Catalog, S3, and Redshift

The exam will likely test your understanding of transforming semi-structured data (like JSON or Parquet), automating Glue job orchestration, and handling schema changes.

Amazon EMR

EMR is AWS’s managed service for big data processing frameworks like Apache Spark, Hadoop, Hive, and HBase. EMR offers more control and flexibility than Glue but requires cluster management.

Key exam topics include:

Choosing instance types and autoscaling clusters
Using Spark for transformation and aggregation workloads
Integrating EMR with S3 for storage and with Glue Catalog for metadata
Optimizing performance using partitioning, compression, and memory tuning

You must also understand when EMR is a better choice than Glue, for example, in use cases requiring custom Spark libraries or long-running stateful processing.

Real-Time Processing with Kinesis and MSK

Streaming data analytics is another critical capability covered in this domain. Amazon Kinesis and Amazon MSK (Managed Streaming for Apache Kafka) are the two primary services used to process data in near real time.

Kinesis includes:

Kinesis Data Streams for ingesting high-volume, time-series data
Kinesis Data Firehose for loading data into S3, Redshift, or Elasticsearch without coding
Kinesis Data Analytics for real-time SQL-based transformations on streaming data

MSK is a fully managed Kafka service that offers more flexibility for advanced streaming workloads. You’ll need to know:

When to use Kinesis vs. MSK
How to scale stream consumers
How to design for replay and fault tolerance in stream processing

Streaming use cases are often scenario-based in the exam, such as log processing, IoT telemetry, and event-driven pipelines.

Automating and Operationalizing Data Pipelines

Another key exam focus is automating and managing data pipelines. Candidates must understand:

How to orchestrate ETL jobs using triggers and workflows in Glue
How to schedule and monitor jobs for failure recovery
CI/CD concepts for deploying processing logic
Logging and alerting using CloudWatch and CloudTrail

Expect questions on how to ensure high availability, scalability, and cost-efficiency when running processing workloads. Automation is not just a DevOps concern—it’s part of ensuring reliable analytics systems.

The Role of Analysis and Visualization in the Certification

Once data is processed, it’s ready for analysis. This domain evaluates your ability to select and integrate tools that turn raw data into meaningful insights. AWS offers native services and integrates with third-party tools for dashboards, ad-hoc querying, and business intelligence.

Amazon Athena

Athena is a serverless interactive query service that allows users to query data in S3 using standard SQL. It relies on the Glue Data Catalog and supports a variety of formats, including Parquet, ORC, and JSON.

Know how to:

Write efficient queries with partition pruning
Use CTAS (CREATE TABLE AS SELECT) statements to transform datasets
Manage performance and cost (e.g., optimize small files, use compressed columnar formats)
Use Athena for ad-hoc analysis over data lakes.

Athena is well-suited for scenarios that need fast access without provisioning infrastructure.

Amazon QuickSight

QuickSight is AWS’s scalable business intelligence service for building dashboards and reports. It supports multiple data sources, including S3, Redshift, Athena, RDS, and external databases.

You should understand:

How to configure data sets and SPICE (in-memory cache engine)
Creating visualizations from live queries or cached data
Setting up dashboards with filters, drilldowns, and alerts
Sharing and permissioning dashboards securely

QuickSight is commonly tested as part of end-to-end scenarios where a business stakeholder needs access to insights.

Redshift for OLAP Workloads

Redshift is not just a warehouse—it’s also used for analytical reporting. You may need to:

Write complex SQL queries using window functions and aggregations
Optimize tables using distribution and sort keys.
Combine Redshift with BI tools like QuickSight for data exploration

When dealing with structured, high-performance reporting workloads, Redshift remains the default choice.

Choosing the Right Tool for the Right Task

This certification is not about memorizing service names—it’s about making the right architectural decisions. For example:

Use Athena for ad-hoc queries on S3 with little setup
Use EMR for complex data transformations in Spark at scale.
Use Kinesis for ingesting real-time event data. a
Use QuickSight for end-user dashboards and business reporting.g
Use Glue to create managed ETL workflows integrated with the Data Catalog

Scenario-based questions may include options where multiple services seem viable. Understanding cost, performance, and latency will help you pick the best one.

Sample Scenarios and What to Expect

Here are sample question themes likely to appear in the exam:

A company ingests IoT data every second and needs to analyze anomalies in real time. What’s the best combination of services to use?
A team wants to run nightly jobs to clean and prepare data from CSV files stored in S3 and make it available in Parquet format for BI reporting.
A marketing team requires a dashboard that updates every 15 minutes with campaign performance data pulled from multiple sources.

The correct answers typically involve layering services in a way that matches the desired latency, scalability, and cost constraints.

Common Mistakes and Misconceptions

When preparing, be aware of these pitfalls:

Choosing EMR for simple workflows that could be done in Glue
Using Redshift for streaming workloads, where Kinesis is better suited
Ignoring data compression and partitioning in Athena queries, leading to high costs
Misunderstanding the SPICE engine in QuickSight and its role in performance
Confusing Glue jobs with Glue Crawlers—each serves a different purpose

It’s essential to grasp the intent and limits of each service to avoid overengineering or underdelivering.

Final Thoughts

The AWS Certified Data Analytics – Specialty exam reflects real-world decision-making: selecting, integrating, and managing tools across the analytics lifecycle. From raw data ingestion to interactive dashboards, your knowledge of how AWS services work together will be your strongest asset.

You’re not just being tested on how a service works—you’re being asked whether you can build a solution that works.

By now, you’ve covered all four domains of the certification:

Collection
Storage and Data Management
Processing
Analysis and Visualization
Security (reviewed in earlier parts)

Together, these make up a comprehensive view of cloud-native data analytics using AWS. With strong preparation, hands-on practice, and a scenario-driven mindset, you’re well on your way to passing the exam and building advanced analytics solutions in the cloud.