| --- |
| 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. |
