Fabric Architecture Podcast

By Matthias Falland

Architecture decisions for Microsoft Fabric. Anonymized real customer scenarios, cost realism, counter-arguments included. Weekly episodes aligned with Fabric Friday recordings.... more

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FAQs about Fabric Architecture Podcast:

How many episodes does Fabric Architecture Podcast have?

The podcast currently has 23 episodes available.

Fabric Architecture Podcast episodes:

May 29, 2026ML Models in Fabric: Training, Deployment, and When to Stay on Azure ML
ML Models in Fabric: Training, Deployment, and When to Stay on Azure ML
Episode 22 • 2026-05-29
Microsoft Fabric ships its own MLflow registry — but is it a replacement for Azure Machine Learning? Matthias and Fabia work through the four-layer registry model, PREDICT versus Model Endpoints, the Direct Lake prediction loop, and the architectural question that actually determines the answer: where do your predictions land?
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
Where do the predictions land. That question answers the architecture. OneLake plus Power BI Direct Lake — Fabric ML Model, genuinely the right call. REST API for an app — evaluate Endpoints maturity or route to Azure ML. GPU training,...
I'd go further. Already on Databricks with Unity Catalog? Don't migrate. Fabric ML Model is not a migration target for Databricks shops — the platform maturity gap is real. The hybrid that actually works: train on Azure ML with GPU,...
For Power BI shops — yes. PREDICT writes predictions to a Delta table in OneLake, Direct Lake reads it with zero copy, zero scheduled refresh. That eliminates an entire class of ETL work. But only if Power BI is your audience.
Resources
ML Experiment
Notebooks
Lakehouse
Direct Lake
Code-first AutoML
Low-code AutoML
SynapseML
Activator
Machine learning model in Microsoft Fabric
What is Data Science in Microsoft Fabric?
Tutorial Part 3: Train and register a machine learning model
Tutorial Part 4: Perform batch scoring and save predictions
Machine learning model scoring with PREDICT
Serve real-time predictions with ML model endpoints (Preview)
Train models with scikit-learn in Microsoft Fabric
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
11min
May 22, 2026Event Schema Set: Contracts That Stop Midnight Breakage
Event Schema Set: Contracts That Stop Midnight Breakage
Episode 21 • 2026-05-22
Event Schema Set is Fabric's contract layer for streaming data — but it ships in Preview with real gaps. Matthias and Fabia unpack the retrofit trap, the dead-letter gap everyone worries about, and when Confluent Schema Registry is honestly the better call.
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
Treat schemas as append-only contracts. Add fields with defaults — safe. Remove required fields — breaks consumers. Change a type — silent data corruption. Rename a field — silent loss in KQL queries. The system won't stop you. Your...
Fair argument. And honestly? If you're an existing Kafka shop with established Confluent practices — use Confluent. The migration cost isn't worth it. Eventstream can deserialize Confluent-encoded payloads natively. You get Avro plus JSON...
But you operate a separate cluster. Separate auth. Separate billing. If your entire stack is Fabric-native — Eventstream, Notebook, Activator, Eventhouse — the integration is a real win. No client library. No external cluster. Governance...
Resources
Schema Registry — known limitations
CloudEvents 1.0
Use schemas in eventstreams
Real-Time Hub Schemas
Business Events Concepts
Consume Business Events from Activator
Eventhouse
Confluent Kafka source
Schema Registry in Fabric Real-Time Intelligence (preview) — Overview
Create and manage event schema sets
Create and manage event schemas in schema sets
EventSchemaSet REST API definition
Eventstream Overview — Schema Management section
Multiple-Schema Inferencing in Eventstream (Preview)
Eventstream Data Formats: JSON, CSV, Avro
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
12min
May 15, 2026Data Activator: Stateful Alerts That Don't Spam Your Team
Data Activator: Stateful Alerts That Don't Spam Your Team
Episode 20 • 2026-05-15
Data Activator is Fabric's no-code event detection engine — but most teams build it wrong. Matthias and Fabia unpack the stateful rule model, the billing trap everyone hits once, and when Power Automate is actually the better answer.
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
Take-home: the entity hierarchy is the product.
Fair. For low-frequency data — a daily KPI check — it works fine. Where it breaks: ten thousand events per second per rule. Power Automate isn't built for that volume. And a per-flow variable isn't per-object state — you'd need one flow...
Right. Wrong in exactly one place — the state machine. Here's the thing. A stateless rule fires on every matching event. Value greater than twenty-five? Sensor reports every five seconds, stays above twenty-five for an hour — you get seven...
Resources
Add Activator to Eventstream
Activator from KQL Queryset
Activator from RTD
Activator from Power BI
Real-Time Hub Set Alerts
Set Alerts on Anomaly Detection
What is Fabric Activator?
Tutorial: Create and activate a Fabric Activator rule
Create a rule in Fabric Activator
Trigger modeling in Activator
Fabric Activator rules
Detection conditions
Activator Limitations
Latency in Activator
Activator Capacity Consumption
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
10min
May 08, 2026Real-Time Dashboard: When 10-Second Refresh Changes the Architecture
Real-Time Dashboard: When 10-Second Refresh Changes the Architecture
Episode 19 • 2026-05-08
Real-Time Dashboard is not Power BI wearing a different hat. Matthias and Fabia unpack the naming collision, permission separation, Activator alert traps, and when you should actually use Power BI DirectQuery instead.
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
If someone asks 'what's happening right now' — Real-Time Dashboard.
But you lose permission separation. You lose tile-as-query simplicity. And your team will absolutely blame the network when the DirectQuery report takes four seconds to load at scale. Different tools, different tradeoffs.
Fair argument. Power BI can connect to KQL via DirectQuery. You get DAX measures, RLS, the full semantic model. And in Premium, automatic page refresh goes as low as five seconds. So if your team already lives in Power BI — that's a legitimate path.
Resources
KQL Database
KQL Queryset
Real-Time Hub
Activator on RTD
Anomaly Detection
Power BI + KQL
Fabric Map
What is Real-Time Dashboard?
Create a Real-Time Dashboard
Real-Time Dashboard Permissions
Use Parameters in Real-Time Dashboards
Customize Real-Time Dashboard Visuals
Activator Limitations
Generate Real-Time Dashboard with Copilot
Copilot-assisted Real-Time Data Exploration (Preview)
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
9min
May 04, 2026Real-Time Hub: The Yellow Pages Your Streams Were Missing
Real-Time Hub: The Yellow Pages Your Streams Were Missing
Episode 18 • 2026-05-01
Matthias and Fabia unpack Fabric's Real-Time Hub — the tenant-wide catalog that sits above Eventstream, Eventhouse, and Activator. They tackle why it feels redundant until it doesn't, dig into a real Reddit question about skipping the Hub entirely, and lay out the four-layer real-time stack every architect should internalize.
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
So — today's lesson. The Hub is not a processing engine. It's not a new Eventstream. It's the inventory layer that streaming has always been missing. Pattern dictates platform — if your pattern is discovery at organizational scale, this is...
I mean, fair question. If every stream you have lives in one workspace and one team owns them all — the Hub's discoverability value is close to zero. You already know what exists. Same if you're publishing streams to non-Fabric consumers...
Right. And... that's actually fine for small setups. The connector list is identical — same Azure Event Hubs tile, same Kafka tile, same CDC tiles. Both paths end up creating an eventstream artifact. But here's the thing. Eventstream is...
Resources
managed private endpoint
Eventstream Overview
KQL Database
Activator Overview
Real-Time Dashboard
Schema Sets
Digital Twin Builder
Real-Time Hub Overview
Get Started with Real-Time Hub
Supported Sources
Add Azure Event Hubs Source
Add Azure IoT Hub Source
Get Azure Blob Storage Events
Create Streams from Workspace Item Events
Create Streams from OneLake Events
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
12min
May 01, 2026KQL Queryset: Why Pipe-Forward Beats SQL for Time-Series
KQL Queryset: Why Pipe-Forward Beats SQL for Time-Series
Episode 17 • 2026-04-24 Duration: 9:39
Matthias and Fabia explore the KQL Queryset in Microsoft Fabric — why the pipe-forward mental model beats SQL for time-series data, when to use make-series vs bin+summarize, and the architectural decision between KQL Queryset, Notebooks, and the SQL endpoint.
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
So — the lesson. Show me the query pattern. That's it. Don't pick your tool based on what you know. Pick it based on what the data needs. If you're doing time-series at scale, learn the pipe. It's worth it.
I mean, fair question. If your workload is analytical reporting — quarterly trends, executive dashboards, scheduled refresh — Power BI connected through the SQL endpoint is probably the better path. You get a richer visualization library,...
Right. And the naive answer is — just use the T-SQL endpoint, it supports SELECT statements. Which is true. But here's the thing. T-SQL on a KQL database is read-only DQL. SELECT only. No DDL, no management commands. And more importantly —...
Resources
Query data in a KQL queryset
Create a KQL queryset
Kusto Query Language overview
SQL to KQL cheat sheet
KQL quick reference
make-series operator
series_decompose_anomalies()
Anomaly detection and forecasting
Time series analysis
render operator
Share KQL queries
Create a Real-Time Dashboard
Real-Time Intelligence tutorial part 5: Query streaming data using KQL
Tutorial: Learn common operators
Tutorial: Use aggregation functions
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
10min
May 01, 2026KQL Database: Why Time-Series Data Needs Its Own Engine
KQL Database: Why Time-Series Data Needs Its Own Engine
Episode 16 • 2026-04-17 Duration: 10:26
Matthias and Fabia explore why KQL Database exists alongside four other analytical stores in Microsoft Fabric. They unpack the Eventhouse-as-building mental model, the caching vs retention trap, and when you should — and shouldn't — choose KQL over SQL.
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
If your data is time-series, logs, or telemetry — and your queries are always filtered by time — KQL Database isn't just an option.
Fair. And honestly, if your team has strong Python skills and your latency tolerance is minutes, not milliseconds — Lakehouse plus notebooks is a legitimate path. You get the Spark ecosystem, ML libraries, broader tooling. I wouldn't fight...
Right. And that matters for the reversal. Because the naive answer teams land on is: just put your IoT data in the Lakehouse. Delta Lake handles everything, right?
Resources
What is Real-Time Intelligence?
Choose an analytical data store in Microsoft Fabric
Eventhouse overview
Data connectors overview
Get data overview
Change data policies
KQL overview - scalar data types
Eventhouse and KQL Database consumption
Pricing cost drivers
Create a KQL database
Time series analysis
Anomaly detection and forecasting
Manage and monitor a database
Manage and monitor an eventhouse
KQL Database git integration
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
11min
April 10, 2026Eventstreams — When No-Code Streaming Hides the Failure Mode
Eventstreams — When No-Code Streaming Hides the Failure Mode
Episode 15 • 2026-04-10
Duration: 8:03
Matthias and Fabia break down Fabric Eventstreams — the visual stream processor that replaces three Azure services with one canvas. They explore why green doesn't always mean flowing, tackle Kafka compatibility from a real Reddit question, and walk through the four billing meters that confuse every FinOps team.
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
Eventstreams are not about replacing Spark or Kafka.
Fair. If you need stateful ML inference mid-stream, Eventstreams won't do it — route to a Spark Notebook destination instead. And if your team needs exactly-once semantics, at-least-once with deduplication in Eventhouse covers most cases,...
And your team will absolutely say that in the sprint demo.
Resources
Eventstream Overview
Add and manage event sources
Route events to destinations
Edit and publish an eventstream
Route data streams based on content
DeltaFlow output transformation
Monitor the status and performance of an eventstream
Pause and resume data streams
Capacity consumption for Fabric eventstreams
Add Azure Event Hubs source
Add Azure IoT Hub source
Add Eventhouse destination
Add Lakehouse destination
Process events with SQL code editor
Explore and transform bike-sharing data
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
9min
April 03, 2026Eventhouse — Why Your Lakehouse Can't Do Real-Time
Eventhouse — Why Your Lakehouse Can't Do Real-Time
Episode 14 • 2026-04-03
Duration: 7:32
Matthias and Fabia break down Microsoft Fabric's Eventhouse — when you need a dedicated real-time store, how hot and cold cache tiers drive your bill, and why the default cache policy is the most common Eventhouse mistake.
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
The architectural principle to take home
Key takeaways
Don't default your real-time data into the Lakehouse just because it's familiar.
Completely. If your latency tolerance is five to thirty seconds and you already know Spark — that's a defensible architecture. Eventhouse costs you KQL ramp-up, a separate billing model, one more item to govern. Don't add an engine just...
Right. Now — the naive answer when someone asks 'where do I put streaming data in Fabric' is always the Lakehouse. Everything goes to OneLake. Sounds clean on a slide.
Resources
Eventhouse overview
Store data in Microsoft Fabric - Decision guide
Caching policy (hot and cold cache)
Get data overview
KQL quick reference
Eventhouse and KQL Database consumption
Data availability in OneLake
Enable Eventhouse endpoint for lakehouse and data warehouse
Create an Eventhouse
Create a KQL database
Real-Time Intelligence tutorial
Change data policies
Cost breakdown of Eventhouse
Manage and monitor an eventhouse
Decision guide: Choose the right data store
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
8min
March 27, 2026Apache Airflow in Fabric: When Code-First Orchestration Earns Its Keep
Apache Airflow in Fabric: When Code-First Orchestration Earns Its Keep
Episode 13 • 2026-03-27
Duration: 8:19
When should you reach for Apache Airflow Job instead of Data Pipelines in Microsoft Fabric? Matthias and Fabia break down pool types, cost traps, dbt fragility, and the architectural threshold where visual orchestration breaks down.
What we discuss
A real-world mistake from a pre-Fabric era
The one question that reframes the architectural debate
How we got here — predecessor products and evolution
Why the "obvious" answer is often wrong
A real Reddit/Microsoft Q&A question unpacked
The concrete recommended architecture
F-SKU realism — what this actually costs
When the rejected approach is actually right
Risks of the recommended path
What Microsoft is shipping that changes the calculus
The architectural principle to take home
Key takeaways
I'm stealing that. But yeah — match the orchestrator to the orchestration complexity. If your workflow fits on a visual canvas, keep it there. Airflow is for when Python is genuinely the clearest way to express your pipeline logic.
For maybe... sixty, seventy percent of Fabric workloads, Data Pipeline is the right answer. Airflow earns its spot when you need cross-cloud orchestration, dbt models, complex dependency graphs, or your team already thinks in Python DAGs....
You just paid for a permanent orchestra conductor to wave a baton at six musicians who already know the song.
Resources
What is Apache Airflow Job?
Run a Fabric item using Apache Airflow DAGs
Apache Airflow compute in Fabric
Apache Airflow job pricing
Transform data using dbt
Migrate to Apache Airflow job in Microsoft Fabric
CI/CD for Apache Airflow in Fabric
Apache Airflow Job region availability
Quickstart: Create an Apache Airflow Job
Apache Airflow Job workspace settings
Access Apache Airflow Job Logs
Sync a GitHub repository in Apache Airflow Job
Run Hello-world DAG in Apache Airflow Job
Fabric region availability
MS Learn
About the show
Built on ElevenLabs voice synthesis. Matthias — cloned voice. Fabia — designed AI co-host. See Matthias live on YouTube (Fabric Friday), at his meetups, and at conferences like FabCon.
Hosted by Matthias Falland — Microsoft Data Platform MVP and community architect behind the Fabric Periodic Table. New episodes every Friday.
Submit your case
Have an architecture decision you are wrestling with? DM Matthias on LinkedIn — find him as Matthias Falland. Three to five sentences about the decision, your team size, and your current stack. We anonymize before airing.
Built on ElevenLabs voice synthesis. Brand design based on fabricperiodictable.com.
...more
9min

FAQs about Fabric Architecture Podcast:

How many episodes does Fabric Architecture Podcast have?

The podcast currently has 23 episodes available.