Determine High-Performing Data Ingestion And Transformation Solutions

Exam Guide: Solutions Architect - Associate ⚡ Domain 3: Design High-Performing Architectures 📘 Task Statement 3.5

🎯 Determining High-Performing Data Ingestion And Transformation Solutions is about getting data into AWS, transforming it into useful formats, and enabling analytics at the required speed, scale, and security level.

First decide batch vs streaming ingestion, then pick the right transfer/ingestion service, then pick the transformation engine, then enable query + visualization.

Knowledge

1 | Data Analytics And Visualization Services

Athena, Lake Formation, QuickSight

1.1 Amazon Athena

Serverless SQL queries directly on S3 data (commonly Parquet/ORC for performance).

• Great for ad-hoc querying and quick analytics

• Works best with a catalog like Glue Data Catalog

1.2 AWS Lake Formation

Build and govern a data lake on S3:

• Central permissions model (tables, columns)

• Helps manage who can access which datasets

1.3 Amazon QuickSight

Serverless BI dashboards and visualization:

• Connects to Athena, Redshift, RDS, and other sources

• Used for “business dashboards” exam clues

2 | Data Ingestion Patterns

Frequency

Common patterns:

• Near real-time: events every second (clickstream, IoT telemetry)

• Micro-batch: every minute / every 5 minutes

• Batch: hourly/daily/weekly loads

• One-time migration: initial bulk transfer + then incremental updates

Ingestion frequency often decides Kinesis (streaming) vs DataSync/S3 batch.

3 | Data Transfer Services

DataSync & Storage Gateway

Used when data originates outside AWS or you need managed movement.

3.1 AWS DataSync

Managed, accelerated online transfer (on-prem ↔ AWS):

• Moves large datasets efficiently

• Good for recurring transfers and migrations

3.2 AWS Storage Gateway

Hybrid storage integration (on-prem access with AWS backing):

• File Gateway (NFS/SMB) to S3

• Volume Gateway (block storage backed by AWS)

• Tape Gateway (backup/archive integration)

4 | Data Transformation Services

AWS Glue

Serverless data integration (ETL):

• Crawlers discover schema

• Jobs transform data (Spark-based)

• Common for converting formats (CSV/JSON → Parquet)

“Convert CSV to Parquet” → Glue.

5 | Secure Access To Ingestion Access Points

Typical protection mechanisms:

• IAM roles (least privilege) for producers/consumers

• S3 bucket policies + Block Public Access + encryption

• VPC endpoints / PrivateLink for private service access

• TLS for ingestion endpoints

• KMS keys for encryption at rest

“Data must not traverse the public internet” → VPC endpoints/PrivateLink + private subnets.

6 | Sizes And Speeds To Meet Business Requirements

Match service to throughput:

• Bulk files (TB-scale) → DataSync / Snowball (when offline) / S3 multipart upload

• Continuous events → Kinesis

• Query performance on S3 → store as Parquet, partition by date/key, use Athena

7 | Streaming Data services

Amazon Kinesis

7.1 Amazon Kinesis Data Streams

For real-time streaming ingestion:

• Producers write records to shards

• Consumers process in parallel

• Scales by shard count

“Need real-time stream with custom consumers” → Data Streams

7.2 Kinesis Data Firehose

For “streaming to storage/analytics destinations” with minimal ops:

• Loads to S3, Redshift, OpenSearch, etc.

• Can transform via Lambda in-flight (basic transforms) _ “Just deliver streaming data into S3/Redshift with minimal management”_ → Firehose

Skills

A | Build And Secure Data Lakes

Baseline data lake pattern:

• S3 as storage (raw/clean/curated zones)

• Glue Data Catalog for schema

• Lake Formation for governance (optional but commonly tested)

• Encryption with KMS + tight bucket policies

B | Design Data Streaming Architectures

Common streaming pipeline:

• Producers → Kinesis Data Streams → consumers (Lambda/Kinesis Client) → S3/DB/analytics

Or simpler:

• Producers → Firehose → S3 (often landing as Parquet with later processing)

C | Design Data Transfer Solutions

• Recurring online transfer from on-prem → DataSync

• Hybrid access to S3 from on-prem apps → Storage Gateway (File Gateway)

D | Implement Visualization Strategies

• Query data with Athena

• Visualize in QuickSight

• Secure access with IAM and Lake Formation permissions

E | Select Compute Options For Data Processing

Amazon EMR

Used for big data processing with Spark/Hadoop:

• Highly scalable distributed processing

• Good when you need full control of the data processing framework

“Spark job / Hadoop” → EMR.

F | Select Appropriate Configurations For Ingestion

• Streaming capacity: shard count (Kinesis Data Streams)

• Batch throughput: concurrency, scheduling, compression, multipart uploads

• Choose Parquet + partitioning for query performance

G | Transform Data Between Formats

CSV → Parquet

Common approach:

1 Land raw data in S3 2 Transform with Glue (ETL) into Parquet in a curated zone 3 Query via Athena, visualize via QuickSight

Cheat Sheet

Requirement Choice

Ad-hoc SQL on files in S3 Athena

Business dashboards/BI QuickSight

Govern a data lake with fine-grained permissions Lake Formation

Move lots of data from on-prem to AWS online DataSync

Hybrid file access (NFS/SMB) backed by S3 Storage Gateway (File Gateway)

Transform/ETL and convert CSV → Parquet AWS Glue

Real-time streaming ingestion with custom consumers Kinesis Data Streams

Stream into S3/Redshift with minimal ops Kinesis Data Firehose

Spark/Hadoop processing at scale Amazon EMR

Recap Checklist ✅

1. Choose batch vs streaming ingestion based on frequency and latency needs

2. Pick the right transfer service (DataSync vs Storage Gateway) for hybrid needs

3. Design a secure S3-based data lake (catalog + governance + encryption)

4. Choose the right streaming service (Kinesis Streams vs Firehose)

5. Transform data using Glue (including format conversion like CSV → Parquet)

6. Select compute for processing (EMR when Spark/Hadoop is required)

7. Enable analytics (Athena) and dashboards (QuickSight) securely

AWS Whitepapers and Official Documentation

Analytics And Visualization

Data Ingestion And Transfer

Streaming

Transformation And Catalog

1. AWS Glue
2. Glue Data Catalog

Storage

Amazon S3

Processing

Amazon EMR

🚀