Amazon Managed Workflows for Apache Airflow (MWAA) is the best choice for orchestrating complex, dependency-driven data pipelines. It is a managed service for Apache Airflow, which is specifically designed for workflow orchestration using directed acyclic graphs (DAGs). It is serverless, integrates natively with AWS analytics services (like Redshift, Glue, EMR), and is ideal for migrating existing Airflow workflows or building new, sophisticated ETL orchestration.

Amazon Kinesis Data Streams with enhanced fan-out is designed for this exact scenario. Enhanced fan-out provides dedicated throughput per consumer, allowing multiple applications (like Kinesis Data Analytics for the dashboard and a consumer writing to S3/Redshift) to read the same data from a stream simultaneously without contending for read throughput. It provides high durability, massive scale, and the decoupling required for independent consumer processing.

SageMaker Studio with Elastic Kernel provides the best balance of performance, cost, and operational efficiency. It allows data scientists to choose an instance type (CPU, GPU, memory-optimized) that fits the specific needs of each notebook session. When the notebook is idle, the kernel can be stopped, saving compute costs. This is more flexible and cost-effective than a fixed, always-on notebook instance and is a core benefit of the SageMaker Studio architecture.

Creating a Redshift read replica provides physical isolation for the read-heavy dashboard workload. The replica asynchronously replicates data from the primary cluster. This completely offloads the query load from the primary cluster, protecting the performance of ETL jobs. While WLM can help manage resources, it doesn’t provide full isolation and both workloads still compete for the same underlying hardware. A read replica is a standard pattern for separating analytical query traffic from write/ETL operations.

Redshift Spectrum is the feature that allows a Redshift cluster to run SQL queries directly against exabytes of structured and semi-structured data in Amazon S3. By creating external tables in the AWS Glue Data Catalog, you define the schema and location once, and both Redshift (via Spectrum) and Athena can query the same source-of-truth data in S3 without moving or duplicating it. This is a core capability of the lake house pattern on AWS.

SageMaker Pipelines allows you to define the entire ML workflow (data preparation, training, validation, deployment) as a series of interdependent steps in a DAG. It integrates with SageMaker Model Registry and can use step caching. Crucially, it can explicitly manage the lineage and dependencies between steps, including data artifacts. By codifying the dependency that the training step has on specific, versioned data from the data preparation step, you ensure the pipeline fails early if that dependency isn’t met, preventing the runtime error seen in the post-mortem.

This architecture is serverless, scalable, and fits the real-time requirement perfectly. Kinesis Data Streams durably ingests the high-volume, variable data stream. AWS Lambda scales automatically to process the stream records, and its integration with SageMaker allows it to efficiently invoke the pre-trained NLP model as an endpoint for low-latency inference. This entire pipeline is managed, with no servers to provision, and can handle the spiky, global workload efficiently.

Job bookmarking is a Glue feature that tracks previously processed data. When combined with a partitioned dataset (e.g., by date like `s3://bucket/data/year=2023/month=10/day=05/`), Glue can automatically identify and process only the new partitions that have arrived since the last job run. This is the most impactful optimization as it directly reduces the amount of data scanned (lowering S3 costs) and processed (lowering Glue DPU costs), leading to faster job completion and significant cost savings.

Amazon SageMaker Clarify is specifically designed to provide machine learning explainability. It can compute feature attribution values, such as SHAP (SHapley Additive exPlanations), which indicate how much each input feature contributed to a specific prediction. This can be done during model training (global explainability) and, crucially, for individual inferences made against a live SageMaker endpoint (local explainability), providing the “why” behind each forecast.

Amazon Managed Workflows for Apache Airflow (MWAA) is the best choice for orchestrating complex, dependency-driven data pipelines. It is a managed service for Apache Airflow, which is specifically designed for workflow orchestration using directed acyclic graphs (DAGs). It is serverless, integrates natively with AWS analytics services (like Redshift, Glue, EMR), and is ideal for migrating existing Airflow workflows or building new, sophisticated ETL orchestration.0