Forem: Reiji Otake

[FabCon Atlanta 2026 Report] My Take on Fabric IQ Ontology

Reiji Otake — Wed, 06 May 2026 23:03:45 +0000

I attended FabCon Atlanta 2026.

I also created a few short videos that show the atmosphere of the venue, so feel free to check them out first.

FabCon Atlanta 2026 Day1-Day3 morning workshops 、KeyNone

FabCon Atlanta 2026 Day3 noon-Day5CoreNote session power hour

In this article, based on what I saw and heard at FabCon, I would like to focus especially on Ontology within Fabric IQ and share how I think we should understand it at this point in time.

Fabric IQ is described as a workload that organizes data in OneLake using business language, enabling analytics and AI agents to use that data with consistent meaning.

The Fabric IQ workload includes semantic models and Data Agents, and Ontology is one part of it.

I think many people may currently understand “Fabric IQ” as almost the same thing as “Ontology.”

That is not completely wrong. However, Fabric IQ is a broader term, so in this article I will mainly use the word “Ontology” to avoid confusion.

What is Fabric IQ (preview)?

The Atmosphere Around Fabric IQ at FabCon

At FabCon, I felt that everyone also highly interested in Fabric IQ.

At the same time, some of the questions were very basic, such as “What is IQ?”

In other words, my honest impression was that Fabric IQ is attracting a lot of attention, but even in the United States, understanding of it has not yet become widespread.

I also attended several IQ-related sessions. Based on the sessions I joined, I cannot say that I clearly saw exactly which real-world projects should use it and how.

Of course, there were Ontology demos, and there were discussions about how AI will be able to understand business meaning more easily and how the semantic layer will become more important. Officially, Ontology is also described as a way to represent a business in a machine-readable form through entities, properties, relationships, and rules.

However, to be honest, my current impression is that the concept itself is very attractive, but common implementation patterns are not yet widely understood.

My Conclusion First: I Would Still Take a Wait-and-See Approach for Production Use

My conclusion is that, at this point, I would still take a wait-and-see approach before placing Ontology at the center of a production environment.

The reason is simple.

First, it is still officially in preview.

Second, when it comes to improving the accuracy of Data Agents by giving them business context, I feel that many use cases can already be covered quite well by using semantic models.

A Common Misunderstanding

Ontology allows you to create entities as business objects and define relationships using natural language to represent business meaning.

On the other hand, based on the current specification, you cannot simply write natural-language descriptions for tables and columns inside Ontology in the same way you can with semantic model properties.

Of course, I am not saying that Ontology is unnecessary.

Rather, I believe Microsoft will continue to invest heavily in this area, and I personally have high expectations for it.

However, at least for now, I think the right stage is:

Development teams should try it in a test environment
Organizations should watch it as a future architecture option

On the other hand, I think it is still a little early to talk about adopting it broadly in production right away.

Semantic Models Will Continue to Be Important for AI

So, does that mean the semantic layer is still something for the future?

I do not think so.

Rather, even right now, building a well-designed semantic model is very effective. I also believe that even after Ontology becomes generally available in the future, the importance of semantic models will not disappear.

Officially, Ontology can be generated from semantic models. In other words, it feels more natural to see Ontology not as something that replaces semantic models, but as something that extends business meaning and relationships on top of semantic models as one of its foundations.

What Semantic Models Can Already Do Today

With the arrival of Data Agent, semantic models are no longer just models for BI.

You can specify a semantic model as a data source for a Data Agent, and through Data Agent customization, you can provide business metadata to AI.

For example:

Semantic model
- Use the “Prep for AI” feature
- Write the business meaning of tables and columns in properties such as table names, column names, table descriptions, and column descriptions
- Predefine calculations and business logic with DAX
Data Agent
- Clarify the role of the agent through instructions
- Add descriptions for data sources so that the agent can choose the right source depending on the question
- Use example query sets for expected questions
  - Note: this is not available for semantic models

For more details, I recommend starting with the following documentation.

Semantic model best practices for data agent

Best practices for configuring your data agent

Also, a Data Agent does not necessarily need to have only one data source.

When the data volume is large, or when you want to use example query sets, combining a semantic model with a lakehouse or warehouse can be a very realistic design.

For example:

Store large volumes of data in a lakehouse or warehouse
Organize the metrics and definitions you want AI to use in a semantic model

If you want to add business metadata to each table or column, my personal recommendation at this point is to write it in the semantic model properties.

Data Agent can refer to semantic model properties.

Editing Semantic Model Metadata Properties from a Notebook with Semantic Link in Fabric

When Would Ontology Become Necessary?

At this point, you might think, “Then isn’t a semantic model enough?”

In fact, I think semantic models can cover a large part of many use cases.

That said, based on my current understanding, I feel that Ontology becomes especially useful in the following two scenarios.

In other words, if your use case does not fall into these two patterns, a semantic model may be enough for now.

1. When You Want to Query Across Multi-Layered Relationships Like a Graph

The first case is when you want to ask questions that go across multiple layers of relationships.

Semantic models can also express relationships. However, as the relationships become more complex, the thinking tends to become more JOIN-oriented.

Ontology, on the other hand, uses a graph-based approach, so it seems better suited to graph-like operations such as path exploration.

For example, imagine you have the following tables:

Customers
Orders
Products
Contracts
Support history
Responsible organizations
Related events

If you want to ask, “What is related to this customer?” across multiple business domains, Ontology seems like a more natural way to express that.

In other words, Ontology becomes meaningful when the relationships themselves are valuable, rather than when you only need simple aggregations or KPI questions.

2. When You Want to Treat Historical Data and Real-Time Data as One Business Entity

The second case is when you want to treat historical data and real-time data not as separate systems, but as the same business object.

Officially, Fabric IQ is described as a way to unify data in OneLake using business language and give consistent meaning to analytics and AI agents.

For example:

Recent order events stored in Eventhouse
Historical order data accumulated in Lakehouse

If you want to handle these together in the context of a single business entity such as “Order,” the idea of Ontology seems to be a very good fit.

This feels less like a simple BI model, or physical model, and more like a foundation that helps AI understand the meaning structure of the business, in other words, a logical model.

We Do Not Need to Rush Ontology. For Now, This Is a Preparation Phase

As I have written so far, I believe Ontology has great potential.

However, I personally do not think it is something that must be introduced as the highest priority right now.

Ontology can be seen as a mechanism for strengthening the business meaning layer afterward.

Therefore, rather than seeing it as a foundation that must be introduced from the beginning, it feels more natural to think of it as something that organizations can add after their data platform and semantic organization have reached a certain level of maturity.

In fact, even if you want to use Ontology, there will likely be many cases where the organization’s data itself is not yet ready.

For example:

Required tables do not exist
Key definitions and meanings differ across systems
Tables that should be related cannot be connected cleanly through relationships

In such a state, the problem exists before Ontology can even be built.

That is why I believe the most important thing right now is to prepare and organize the organization’s data so that it can take advantage of Ontology in the future.

Microsoft will likely continue to invest heavily in this area, and the concept of Ontology itself will become increasingly important.

In that sense, I think we should see the current phase not as “the time to rush Ontology into production,” but as a preparation period for creating the conditions where Ontology can be used effectively.

In addition, I also feel that building Ontology requires a surprisingly high level of skill.

It is not enough to have only data modeling knowledge.

You need both:

An understanding of the business meaning behind the organization’s operations and data
The data modeling knowledge required to turn that meaning into a structure

In other words, Ontology cannot be built only by the IT department.

At the same time, it also cannot be fully defined only by the business department.

Collaboration between IT and business will be important, and people who understand both sides to some extent will become increasingly valuable.

Bonus 1: Foundry IQ Already Feels More Practical

As a side note, based on my experience, Foundry IQ felt more practical at this point.

For example, use cases such as the following are relatively easy to imagine even now:

Using OneLake as a knowledge source
Using SharePoint as a knowledge source

Fabric Ontology still looks like something that may become very interesting in the future.

On the other hand, Foundry IQ already feels easier to connect to concrete use cases.

Of course, these two are not competitors. I believe they will become more connected over time.

Bonus 2: Data Agent Development Works Well with CI/CD and Should Use Git Integration

This is slightly separate from Ontology, but through FabCon, I was reminded again that Data Agent works very well with CI/CD.

Are you using Git integration in Fabric?

As mentioned earlier, when developing a Data Agent, you define items such as instructions, data source descriptions, and example query sets.

Among these, data source descriptions may not change very frequently.

However, I feel that instructions and example query sets are things that will continue to evolve once the agent starts being used.

For example, in actual operation, the following situations are likely to happen:

A user asks an unexpected question, and you want to add a query set for that pattern
You adjust the instruction prompt, but the accuracy becomes worse
You want to roll back to a previous version and check the behavior
You want to compare the previous version and the latest version while testing

In other words, a Data Agent is not something you configure once and forget.

It is something that should be continuously improved during operation.

That is why it works very well with Git integration, where you can manage change history, track differences, and roll back when necessary.

If you want to use Data Agent seriously in Fabric, I believe it is important not only to create the agent, but also to grow it with Git integration in mind.

Microsoft Fabric Git Integration × Azure DevOps: How to Release Fabric Items Across Different Tenants

How to Reflect Changes to Another Repository with Azure DevOps Pipeline: A Minimal Memo for Repo A → Repo B

Summary

Finally, here is my current understanding.

Expectations for Fabric IQ / Ontology are high
However, it is still in preview, so I would be cautious about using it in production at this stage
In many cases, the combination of semantic models and Data Agent is already quite effective
Ontology will become especially useful in scenarios such as:
- Queries across multi-layered relationships
- Use cases where accumulated data and real-time data need to be handled in one business context

I believe this is definitely an area where Microsoft will continue to invest.

Therefore, now is a good time to catch up on Ontology and prepare your organization’s data platform so that you can adopt it quickly when the right timing comes.

Thank you for reading this long article!

I Also Have a YouTube Channel!

https://www.youtube.com/@msfabricreijiotake

Fabric & Databricks Interoperability (4): Using Databricks Tables in Fabric for Viewing, Analysis, and Editing

Reiji Otake — Sun, 16 Feb 2025 10:52:27 +0000

Introduction

Is it possible to seamlessly reference and edit tables created in Fabric within Databricks?

Many people may have this question.

In this article, we will specifically explore the use case of:

Utilizing tables created in Databricks within Fabric.

For prerequisite settings and configurations, please refer to previous articles.

This article is part of a four-part series:

Overview and Purpose of Interoperability

Detailed Configuration of Hub Storage

Using Tables Created in Fabric in Databricks

Using Tables Created in Databricks in Fabric (this article)

Linking Tables Created in Databricks to Fabric

Creating a New Table in Databricks

Create a new empty external table from Databricks.

Specify the folder path of the hub storage as the Location.

CREATE TABLE create_from_Databricks_sales
USING DELTA
LOCATION 'abfss://<container_name>@<ADLS2_name>.dfs.core.windows.net/folder_name/create_from_Databricks_sales'

Checking the Created Table

You can verify that the external table created from the Catalog Explorer contains data.

A folder named create_from_Databricks_sales is created in the ext folder of the hub storage.
(This means that the newly created external table physically exists in the hub storage.)

It can also be confirmed that the table is in Delta format.

At this point, the create_from_Databricks_sales table also becomes visible from Fabric's Lakehouse.

Viewing and Analyzing Tables Created in Databricks in Fabric (Creating BI)

From the Semantic Model, select the create_from_Databricks_sales table (created in Databricks) and click [Confirm].

Now, the table created in Databricks can be analyzed in Fabric.

Editing (DML) Tables Created in Databricks from Fabric

Execute an UPDATE statement (DML statement) from Fabric's Notebook.

UPDATE Fabric_Lakehouse.ext.create_from_Databricks_sales
SET Item = 'No.1 Quantity Water Bottle - 30 oz.'
WHERE Item = 'Water Bottle - 30 oz.'

Of course, it was confirmed that changes were reflected from Fabric.

Editing was performed from Fabric, and the changes were also reflected on the Databricks side.

Therefore, it is possible to edit (DML statements) in Fabric for tables created in Databricks.

Issues and Specific Operational Methods

The method introduced here has the advantage that both Fabric and Databricks can edit data. However, this can also be a weakness, as it makes table updates too easy.

Additionally, in this case, an external table in Databricks was used.

However, predictive optimization is currently only available for managed tables, making it ideal to use managed tables rather than external ones.

Predictive optimization for Unity Catalog managed tables | Databricks on AWS

Learn how predictive optimization improves data layout and query performance for Unity Catalog managed tables on Databricks.

docs.databricks.com

We will continue to examine these challenges and share specific operational methods in the future.

I also think that table cloning in Databricks might provide some useful hints.

Databricksにおけるテーブルのクローン #deltalake - Qiita

Clone a table on Databricks | Databricks on AWS [2022/10/28時点]の翻訳です。本書は抄訳であり内容の正確性を保証するものではありません。正確な内容に関しては原文を参照ください。 cloneコマンドを用いてD...

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Conclusion

Based on the above,

it was confirmed that "tables created in Databricks can be used in Fabric."

Once the hub storage is set up, achieving interoperability between Fabric and Databricks is relatively simple.

▽ Previous article

FabricとDatabricksの相互運用性③：Fabric で作成したテーブルをDatabricksで利用する（Databrickで閲覧・分析・編集可能） #BI - Qiita

この記事は以下の記事の補足記事となります。【総集編】Microsoft Fabric と Databricksをつなぐデータ総合運用術 -hubストレージにDelta Lake形式で保管する- YouTubeのアーカイブでも、16分57秒~あたりからこの記事に...

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Fabric & Databricks Interoperability (3): Using Fabric Tables in Databricks for Viewing, Analyzing, and Editing

Reiji Otake — Sun, 16 Feb 2025 10:34:06 +0000

Introduction

Can tables created in Fabric be seamlessly referenced and edited in Databricks?

Many people may have this question.

In this article, we will specifically introduce the use case of:

Using tables created in Fabric within Databricks.

For details on the prerequisite settings, please refer to the previous article.

This article is part of a four-part series:

Overview and Purpose of Interoperability

Detailed Configuration of Hub Storage

Using tables created in Fabric within Databricks (this article)

Using tables created in Databricks within Fabric

Linking Tables Created in Fabric to Databricks

Creating a New Table in Fabric

Upload a CSV file to the Fabric Lakehouse.

:::note info
The CSV file used in this article is sales.csv from the following Microsoft documentation:

Create a Microsoft Fabric Lakehouse
:::

From the CSV file, select [Load to Table] > [New Table].

Specify ext, which is a shortcut created in the hub storage, as the schema.

Verifying the Created Table

You can confirm that a new table has been created in the Lakehouse.

A create_from_fabric_sales folder is created in the ext folder of the hub storage.

(This means that the newly created table physically exists in the hub storage.)

You can also confirm that the table is in Delta format.

At this point, as expected, the table created in Fabric is not yet visible in Databricks.

Enabling Databricks to Access Fabric Tables

Use the Databricks SQL Editor to create an external table.

Specify the hub storage folder path (the folder of the table created in Fabric) in the Location field.

CREATE TABLE <table_name>
USING DELTA
LOCATION 'abfss://<container_name>@<ADLS2_name>.dfs.core.windows.net/folder_name/<table_folder_name>'

Then, you can view tables created in Fabric from the [Catalog].

Viewing and Analyzing Tables Created in Fabric with Databricks (BI Creation)

From the [Dashboard] in Databricks, you can create a new dashboard and select an external table (i.e., a table created in Fabric) from [Data] > [Select Table].

Thus, it is possible to analyze tables created in Fabric using Databricks.

Editing Tables Created in Fabric with Databricks (DML)

Try executing an UPDATE statement (DML statement) from the SQL Editor in Databricks.

UPDATE create_from_fabric_sales
SET Item = 'No.1 Item'
WHERE Item = 'Road-150 Red, 48'

Of course, you can confirm that the changes have been reflected on the Databricks side.

Although the edit was made from Databricks, the changes were successfully reflected on the Fabric side as well.

SELECT Item, SUM(Quantity * UnitPrice) AS Revenue
FROM Fabric_Lakehouse.ext.create_from_fabric_sales
GROUP BY Item
ORDER BY Revenue DESC;

Therefore, it is possible to edit tables created in Fabric using Databricks (DML statements).

Conclusion

From the above, we have confirmed that

"Tables created in Fabric can be used in Databricks."

Once the hub storage is set up, it is relatively easy to achieve interoperability between Fabric and Databricks.

In the next article, we will introduce the reverse case:

"Using tables created in Databricks in Fabric."

▽ Next article

FabricとDatabricksの相互運用性④：Databricks で作成したテーブルをFabricで利用する（Fabricで閲覧・分析・編集可能） #SQL - Qiita

この記事は以下の記事の補足記事となります。【総集編】Microsoft Fabric と Databricksをつなぐデータ総合運用術 -hubストレージにDelta Lake形式で保管する- YouTubeのアーカイブでも、13分22秒~あたりからこの記事に...

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▽ Previous article

FabricとDatabricksの相互運用性 ②：FabricとDatabricksをハブストレージ（Delta Lake）で接続する設定手順 #Azure - Qiita

この記事は以下の記事の補足記事となります。【総集編】Microsoft Fabric と Databricksをつなぐデータ総合運用術 -hubストレージにDelta Lake形式で保管する- YouTubeのアーカイブでも、9分45秒~あたりからこの記事につ...

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Fabric & Databricks Interoperability (2): Configuring Hub Storage

Reiji Otake — Sun, 16 Feb 2025 09:48:01 +0000

Introduction

This article provides a detailed guide on how to configure settings for the following use cases:

Using tables created in Databricks within Fabric
Using tables created in Fabric within Databricks

:::note info
This article is part of a four-part series:
1. Overview & Purpose of Interoperability

2. Detailed Configuration of Hub Storage (This Article)

3. Using Tables Created in Fabric within Databricks

4. Using Tables Created in Databricks within Fabric

:::

Preparing Azure Data Lake Gen2 (ADLS2) as the Hub

① Deploy a storage account in Azure Portal as the hub

:::note warn
Enable hierarchical namespace.
:::

② Create a container named 'hub' and a directory named 'ext'

Connecting Fabric to Hub Storage

① Create a Lakehouse

:::note warn
Enable Lakehouse schema (public preview).
:::

② Specify the hub storage in the new schema shortcut of the Lakehouse

From [Tables] in the Lakehouse, click the three-dot menu and select [New Schema Shortcut].

Select [Azure Data Lake Gen2].

Enter the details for creating a new connection.

:::note info

How to check ADLS2 access URL:

You can confirm it from the storage account's [Endpoints] section under 'Data Lake Storage'.

:::

Enable the 'ext' directory and click [Next].

Click [Create].

The 'ext' directory is created as an external schema shortcut.

Connecting Databricks to Hub Storage

① Create an access connector for Azure Databricks in the Azure Portal

Follow the steps under "Step 1: Create an Access Connector for Azure Databricks" in the "Use Azure Managed Identity to Access Storage in Unity Catalog" guide, using a system-assigned managed identity.

② Grant the connector access to the hub storage from the Azure Portal

Follow "Step 2: Grant Managed Identity Access to the Storage Account" in the same guide.

③ Create storage credentials in Databricks

After creation, click the newly created credential name.

Click [Permissions] > [Grant].

Grant [ALL PRIVILEGES] to necessary users.

:::note info
Reference for steps and required permissions:

Create Storage Credentials for Connecting to Azure Data Lake Storage Gen2
:::

④ Add an external location in Databricks

:::note info

How to determine the URL:

Refer to the storage account's [Endpoints] section used in step ② of "Connecting Fabric to Hub Storage".
:::

:::note info
Reference for steps and required permissions:

Create an External Location to Connect Cloud Storage to Azure Databricks
:::

Conclusion

Now everything is set up!

Next, let's proceed with the actual interoperability of tables.

▽ Next Article

FabricとDatabricksの相互運用性③：Fabric で作成したテーブルをDatabricksで利用する（Databrickで閲覧・分析・編集可能） #BI - Qiita

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最新版の記事にアップデートしているのでそちらをご覧ください。【総集編】Microsoft Fabric と Databricksをつなぐデータ総合運用術 -hubストレージにDelta Lake形式で保管する- YouTubeのアーカイブでも、この記事について...

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Fabric & Databricks Interoperability (1): Purpose of Hub Storage for Table Sharing

Reiji Otake — Sun, 16 Feb 2025 09:38:46 +0000

Introduction

Although they each have their own characteristics, Microsoft Fabric and Databricks are broadly similar in what they can do.

Through Azure Databricks Unity Catalog mirroring, we are now able to reference Databricks-managed data in Fabric, but editing the data is still not possible.

This brings up the following concerns:

Is it possible for our department to use tables managed by other departments in Databricks via our Fabric?
I want to make the tables I create open for modification and reference, regardless of the tool used!
We are currently using Databricks, but we might migrate to Fabric in the future... we want to maintain a vendor-free stance.
Business-side employees use Fabric, but engineers use Databricks; there are times when we need to reference the same table.

In this article, I will introduce use cases for seamlessly utilizing Fabric and Databricks:

Using tables created in Databricks in Fabric
Using tables created in Fabric in Databricks

The goal of this article:

:::note info
This article consists of four parts:

Overview and purpose of interoperability (this article)
Detailed setup of hub storage
Using tables created in Fabric in Databricks
Using tables created in Databricks in Fabric :::

Prerequisite: Fabric and Databricks have similar functions... which one should we actually use?

Fabric and Databricks are both attracting attention as Lakehouse platforms that handle data end-to-end.

As someone new to the industry, my first impression of using both was that "They can probably do about the same things?".
They both support ETL processes and AI model creation.

Fabric is appealing because of its beginner-friendly GUI, designed for intuitive operations.
On the other hand, Databricks is more code-based, so it seems to require a slightly higher skill level.
Additionally, Databricks offers more customization options for computer resources, and if you stop the cluster frequently, it can be more cost-effective than Fabric.

I believe the choice between these platforms depends on whether you prioritize ease of use or flexibility.

▽Reference

Azure Databricks と Microsoft Fabric の関係を考える🧐 #PowerBI - Qiita

はじめに本記事はDatabricks アドベントカレンダー2024 7日目の記事です。本記事ではAzure Databricksを扱っています。投稿日時点ではDatabricks on AWSなどでは利用できない機能もでてきます。 Azure Dat...

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Considering interoperability methods

Here, I will explore methods for achieving interoperability between Fabric and Databricks.

① Unity Catalog Mirroring (Currently DML from Fabric to Databricks is not supported)

Using Azure Databricks Unity Catalog mirroring (preview), it is possible to reference (SELECT statements) Databricks tables from Fabric, but editing (DML statements) is currently not supported.

Thus, this method is not suitable for interoperability.

Please let me know if my understanding is incorrect 🙇

② Specifying OneLake as an external location from Databricks (Currently not supported)

I tried configuring an external location for OneLake via cloud storage connection to Azure Databricks, but it is not currently supported.

I was hoping this method would work, but unfortunately, it doesn't...
So this method is also not suitable for interoperability.

▽Reference

Azure Databricks から OneLake 上のデータにアクセスする方法 2024/12 版 #Microsoft - Qiita

はじめに Azure Databricks の Unity Catalog ミラーリングを通して、Databricks の管理するデータについて Fabric で利用できるようになりましたが、Fabric レイクハウスなどの OneLake に格納されたデータに Azu...

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③ Hub storage as a solution

Since mirroring and external locations didn't work, I decided to store the actual tables in Azure Data Lake Gen 2 (ADLS2).
By using a schema shortcut to ADLS2 from Fabric, and specifying ADLS2 as the storage location in Databricks' catalog, both Fabric and Databricks can perform SELECT and DML operations.

This means that interoperability between Fabric and Databricks is now possible!

From this point forward, I will refer to this ADLS2 as hub storage.

▽For the specific setup method, refer to the following article:

FabricとDatabricksの相互運用性 ②：FabricとDatabricksをハブストレージ（Delta Lake）で接続する設定手順 #Azure - Qiita

qiita.com

Hub storage works thanks to Delta Lake

As explained above, hub storage allows interoperability between Fabric and Databricks.

But why does this interoperability work?

The key lies in Delta Lake, the mechanism behind it.

The mechanism of Delta Lake

Delta Lake is an open-source storage layer that provides transaction and schema management on top of a data lake. It uses a combination of Parquet and JSON as its underlying data formats. Parquet is a columnar compression format that enables fast queries and data compression, while JSON is used as a transaction log to record data change history and versioning.

By leveraging the Delta Lake mechanism, hub storage enables advanced data sharing and operations. When using Fabric or Databricks, it’s crucial to understand the underlying infrastructure to fully take advantage of the features provided by Delta Lake.

▽For more details on Delta Lake, refer to the official documentation:

Azure Databricks の Delta Lake とは - Azure Databricks | Microsoft Learn

Databricks レイクハウスに電源を供給するために使用される Delta Lake ストレージプロトコルについて説明します。

learn.microsoft.com

Conclusion

In this article, I summarized the purpose and methods of interoperability between Fabric and Databricks.

The next article will provide a detailed guide for setting up hub storage.

▽Next article:

FabricとDatabricksの相互運用性 ②：FabricとDatabricksをハブストレージ（Delta Lake）で接続する設定手順 #Azure - Qiita

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How a New Graduate with No Industry Experience Passed the Databricks Certified Data Engineer Associate Exam in 3 Weeks

Reiji Otake — Sun, 16 Feb 2025 07:56:12 +0000

Introduction

As the title suggests, I am posting this as a memorandum to keep a record of my learning.

It's just a simple summary, but I hope it can be helpful for your studies as well.

If you have any additional questions, please feel free to leave a comment.

Personal Impressions & Key Points

Grasping the Overall Picture

By reading the Databricks Quick Start Guide, you can get an overview of the exam's flow and key points.

Dealing with SQL Questions

For those who have obtained Oracle Silver, the SQL section is relatively easy.

There were more than five SQL questions on the exam.

Utilizing the Databricks Environment

If you have access to a Databricks environment, actually using it and working through tutorials will deepen your understanding.

Fortunately, I had access to a study environment where I could freely experiment.

Leveraging Official Documentation

Going back to the official documentation is ultimately the fastest way to find answers.

In my case, I had a shallow understanding of Unity Catalog, DLT, structured streaming, and Git, so I studied by reading the official documentation.

Study Timeline

24/12/23 Asked Senior Employees Who Passed for Recommended Study Materials

Databricks Certifications: I Took Them All and Summarized Systematically

2024/12/27 Databricks Quick Start Guide

Databricks Quick Start Guide

Recommended for getting a broad overview.

Recommended Points

Affordable Price

The Kindle version is available for 99 yen (this is the regular price, not a sale).

The same content is available for free on Qiita, but the Kindle version is compiled into a single volume, making it significantly easier to read. (Well worth the 99 yen.)

Reliable Information Source

Published by Databricks Japan, ensuring high reliability.

Easy-to-Understand Explanations

Clearly explains the background and history of Lakehouse.

Practical Content

Includes ETL hands-on exercises, making it easy to apply in real-world scenarios.

Caution

Since it was published in 2022, some information may be slightly outdated.

2025/1/7~8 Official Practice Questions

Translated & Explained Databricks Certified Data Engineer Associate Practice Questions

Questions 1-30 have been translated into Japanese and explained.

Unofficial Explanation of Databricks Certified Data Engineer Associate Practice Exam Answers (As of January 2024)

Questions 31-45 are in English, but explanations are provided in Japanese.

2025/1/8~6 Udemy Course Recommended by Senior Employees

Practice Exams: Databricks Certified Data Engineer Associate

Accuracy Rates:

Practice 1: 51% (23/45)

Practice 2: 62% (28/45)

2025/1/16~19 Struggled with Structured Streaming, Delta Live Tables, and Git as a New Graduate

I found them too difficult to understand, so I searched through documentation and tutorials.

Relevant Resources:

Git/GitHub Resources:

2025/1/20 Retook the Official Practice Exam

Accuracy Rate: 86%

2025/1/20 Registered for the Exam

Databricks Certified Data Engineer Associate Exam Registration

Warning:

I do not recommend using a JCB card for payment.

(My confirmation email never arrived, but the payment was deducted. The follow-up with support was very time-consuming.)

1/22 Passed the Exam!! 🎉

Conclusion

Even as an industry newcomer with only 9 months of experience, I was able to pass on the first attempt!

I encourage you to give it a try!

Since then, I have continued using Databricks in my work and have found that the knowledge gained from certification studies has been directly applicable.

This was just a brief summary, but I hope it helps with your studies!