A Quick Look

Capabilities

Ascend

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Multi-Cloud Capabilities

Run on Microsoft Azure, Google Cloud, or AWS ad leverage compute across your environments

AWS-only—locked into AWS ecosystem

Ingestion

Form based connectors, plus custom API ingestion

Crawlers for schema discovery, manual connector setup

Transformation

SQL & Python with AI assistance, visual and code-based development

PySpark/Scala required, code-heavy with Glue Studio UI

Orchestration

Event-driven, metadata-powered automation—no Step Functions needed

Manual orchestration via AWS Step Functions or EventBridge

Observability

Real-time insights, full lineage, built-in error detection

CloudWatch logs and basic job monitoring (good luck debugging)

AI & Automation

Context-aware AI agents automate code generation, troubleshooting, documentation

None—you're writing Spark jobs from scratch

Pricing

Consumption-based pricing on compute you control

MAR-based pricing that scales with data volume (can escalate quickly)

Developer Experience

SQL & Python native, low-code options, Git integration, instant feedback

Spark expertise required, long cold starts, IAM complexity

Scalability

Smart automation scales pipelines without manual tuning

Scales well if you're a Spark expert and have time to optimize

Where AWS Glue Shines

AWS Glue has its strengths, especially for teams deeply invested in AWS:

If you live and breathe AWS, Glue fits naturally into your ecosystem. It talks to S3, RDS, Redshift, Athena, and the rest of the AWS family without leaving home. No API keys to manage across cloud providers, no cross-cloud networking headaches.

Glue abstracts away the infrastructure management of running Spark jobs. You don't provision clusters or manage nodes—AWS handles that for you. For Spark-savvy teams, this is powerful.

You can dial DPUs (Data Processing Units) up or down based on workload. Need more horsepower for a big batch job? Scale up. Running something lightweight? Scale down. (Just watch that bill.)

Where AWS Glue Falls Short

AWS Glue works—until it doesn't. Here's where teams start looking for alternatives:

Multi-cloud strategies aren't an option

Your pipelines are married to AWS. Want to run workloads in BigQuery? Too bad. Need to orchestrate jobs on Databricks? Sorry. Once you're in, you're in.

Debugging and maintaining pipelines is painful

Glue Studio gives you a visual interface, but underneath it's still PySpark or Scala. If your team doesn't know Spark internals, you may struggle. And when jobs fail, you'll be sifting through CloudWatch logs with no real lineage or debugging tools.

IAM Complexity

AWS IAM is... let's call it "powerful." And by powerful, we mean you'll spend hours (or days) figuring out which roles, policies, and permissions your Glue jobs need to access S3, read from RDS, write to Redshift, and talk to Secrets Manager.

Slow, tedious, manual building eats up valuable engineering time

Writing Glue jobs means writing Spark code from scratch. There's no AI to help generate code, no intelligent suggestions, no automated optimization. You're staring at a blank editor hoping you remember the right PySpark syntax.

Cold starts and latancy

Serverless Spark sounds great until you hit cold start times. Glue jobs can take 5–10 minutes just to spin up before processing data. For near-real-time or event-driven workflows, that's a dealbreaker.

Included in Every Plan

Every Ascend plan includes full access to the platform — no feature gating, no user limits. Build, automate, and scale with confidence using the entire suite of AI-powered tools.

Data Ingestion

Say goodbye to fragile handoffs and brittle ingest jobs.

Whether you’re moving data from lakes, warehouses, databases, APIs, or legacy systems, Ascend makes it easy. Flexible connectors and dynamic schema handling ensure your pipelines stay reliable as systems evolve.

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Transformation

Transform Data with Speed at Scale

Ascend automates orchestration using rich metadata and event-driven triggers—so pipelines run only when needed, and custom logic is easy to build in.

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Orchestration

Orchestration That Writes Itself

Monitor pipeline performance, trace lineage end-to-end, and debug faster with real-time, metadata-powered insights.

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Observability

See Everything. Fix Anything.

Run faster, spend less—Ascend uses metadata and fingerprinting to cut unnecessary compute without sacrificing performance.

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DataOps

Built-In DataOps for Modern Teams

From Git-backed workflows to access controls and secure deployment environments, Ascend simplifies operational excellence with native support for modern DevOps practices.

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Say Goodbye to Fragile Handoffs and Brittle Ingest Jobs

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Transform Data
with Speed at Scale

From SQL to Python, declarative to imperative, Ascend supports multiple authoring modes — all integrated with version control and modular pipeline design. Build fast, iterate safely.

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Orchestration
that Writes Itself

Ascend’s DataAware engine lets you design intelligent workflows using the metadata you already have — turning your designs into powerful automation. Ascend's Smart Tables process data up to 100x faster.

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See Everything.
Fix Anything.

From real-time monitoring to full lineage and change tracking, Ascend gives you deep visibility into your data flows — so you can spot issues fast and optimize with confidence.

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Built-In DataOps
for Modern Teams

From Git-backed workflows to access controls and secure deployment environments, Ascend simplifies operational excellence with native support for modern DataOps best practices.

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Intelligence at the Core

Ascend's Intelligence Core combines metadata, automation, and AI in a layered architecture that empowers teams to build pipelines 7x faster and cut processing costs by 83%.

Unified Metadata Collection

Ascend tracks data, code, and user actions in a unified metadata layer — powering automation, observability, and auditability through the entire data lifecycle.

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DataAware™ Automation Engine

Ascend’s automation engine uses rich metadata to orchestrate pipelines — managing dependencies, triggering tasks, and supporting custom event-driven workflows.

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Integrated AI Agents

Let AI do the tedious parts of data engineering — Ascend’s context-aware agents suggest code, explain logic, generate docs, and even automate steps on your behalf.

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