---
language: ja
license: cc-by-4.0
task_categories:
  - question-answering
  - text-generation
tags:
  - data-engineering
  - japanese
  - instruction-tuning
  - sft
  - pretraining
  - synthetic
pretty_name: データエンジニア2026 学習データセット（日本語）
size_categories:
  - 1K<n<10K
---

# Data Engineer 2026 (Japanese) Dataset

A Japanese-language dataset for training and fine-tuning models on modern data engineering topics as of 2026. It contains instruction-tuning (SFT) Q&A pairs and a pretraining corpus covering distributed processing, streaming, data lakehouse, orchestration, transformation, local analytics, modeling, quality and governance, cloud and data platforms, MLOps, and AI/agentic data workflows.

## Dataset splits

| Split | Size | Description |
|---|---|---|
| `sft` | 1,780 samples | Instruction-following Q&A pairs in Japanese (`instruction` / `input` / `output`). Covers definitions, comparisons, how-tos, design and best practices, debugging, interview-style questions, 2026 trends, outlines, and aspect-specific questions. |
| `pretrain` | 186 samples | Japanese technical explanatory text (chunked articles and notes) about data engineering topics, intended for continued pretraining. |

## Schema

### SFT (`sft`)

| Column | Type | Description |
|---|---|---|
| `id` | str | Sample ID (`sft_N`) |
| `category` | str | High-level category (分散処理エンジン, ストリーミング基盤, DWH・SQL, テーブル格式, オーケストレーション, 変換, ローカル分析, モデリング, データ運用, クラウド基盤, AI・ML, 現場, トレンド, 移行, 設計, ...) |
| `topic` | str | Topic slug (e.g. `spark`, `flink`, `kafka`, `iceberg`, `airflow`, `dbt`, `duckdb`, `polars`, `lakehouse`, `contracts`, `governance-ai`, ...) |
| `qtype` | str | Question type (`faq`, `definition`, `overview`, `design`, `howto`, `debug`, `trend`, `interview`, `compare`, `outline`, `aspect`, `variant`) |
| `aspect` | str | Aspect (`general`, `全体像`, `設計`, `パフォーマンス`, `コスト`, `運用`, `セキュリティ`, `移行`, `2026年動向`) |
| `difficulty` | str | `intermediate` (default) |
| `instruction` | str | Question or instruction in Japanese |
| `input` | str | Optional context (empty in this release) |
| `output` | str | Answer or explanation in Japanese (markdown) |
| `lang` | str | `ja` |
| `source` | str | `curated+synthetic` or `synthetic` |

### Pretrain (`pretrain`)

| Column | Type | Description |
|---|---|---|
| `id` | str | Sample ID (`pre_N`) |
| `text` | str | Explanatory Japanese text (markdown-style article or FAQ block) |
| `topic` | str | Topic slug |
| `category` | str | Category |
| `lang` | str | `ja` |
| `source` | str | `synthetic` |

## Coverage (key topics)

- Distributed engines: Apache Spark 4.x, Apache Flink 2.x
- Streaming: Apache Kafka 4.x (KRaft), streaming fundamentals, CDC (Debezium), streaming databases (RisingWave / Materialize)
- Lakehouse and table formats: Apache Iceberg, Delta Lake, Hudi, catalogs (Unity Catalog / Polaris / Gravitino / Nessie), file formats (Parquet / Arrow / Avro)
- Warehouses and SQL engines: BigQuery, Snowflake, Redshift, Trino, StarRocks, ClickHouse, SQL craft
- Orchestration and transformation: Apache Airflow 3.x (Task SDK), Dagster (SDA), dbt (Fusion / Core v2.0), SQLMesh
- Local and modern analytics: DuckDB 1.5 / 2.0, Polars, Python data stack 2026 (pandas 3.x, uv, ruff, marimo, pydantic)
- Data modeling and contracts: Kimball / Data Vault / OBT, data contracts (ODCS), data quality (Soda / Great Expectations / Deequ / Elementary), lineage (OpenLineage)
- Cloud and platform: AWS / GCP / Azure data platforms, Kubernetes data (Spark / Flink operators / Strimzi / Kueue)
- AI / ML and data for AI: AI/LLM data pipelines (embeddings / RAG / vector DB), MLOps data (feature store / lineage / versioning), governance for AI agents (MCP / text2SQL)
- Ops / FinOps / reliability, testing / CI, migration, design patterns

All content reflects 2026-era trends (e.g. Iceberg as the open table format, Spark Connect, Flink disaggregated state, Airflow 3, dbt Fusion, DuckDB / Wasm / Iceberg, streaming-first lakehouse, data contracts, agentic data workflows).

## How to load

```bash
pip install datasets
```

```python
from datasets import load_dataset

ds = load_dataset("LoNebula/data-engineer-2026-ja")
print(ds)
# ds["sft"], ds["pretrain"]
```

## Notes and license

- This dataset is primarily synthetically generated from a curated Japanese knowledge base. It is intended for research, educational purposes, and model training or evaluation.
- While care was taken for factual accuracy (aligning to 2026 versions and trends), it may contain minor inaccuracies. Always verify against official documentation.
- License: CC BY 4.0. Retain attribution to `LoNebula` and a link to this dataset when redistributing or building derived work.

## Acknowledgments

Generated with assistance from Kilo / Claude Code tooling. The dataset structure and schema are designed for instruction tuning (SFT) and continued pretraining in Japanese.