Implement Lean Data Virtualization with Spark & Colectica

Implement Lean Data Virtualization with Spark & Colectica.md

Epic: Implement Lean Data Virtualization with Spark & Colectica

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Section 1: Deploy Spark on Kubernetes

Context:
No Spark backend exists. Adding Spark enables scalable federated queries and integrates with existing Kubeflow/JupyterLab.

Todo:

• Install Spark Operator in Kubernetes cluster via Helm.
• Configure Spark to use existing ODBC drivers (add JDBC if needed).
• Test Spark job submission via Kubeflow notebook.

Expected Outcome:
Spark runs on Kubernetes, ready for federated queries. Zero new infrastructure beyond operator.

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Section 2: Integrate Colectica with Spark

Context:
Colectica manages DDI metadata. Integrating with Spark enables auto-enrichment of queries with statistical context.

Todo:

• Package Colectica Python SDK with Spark environment (via container image).
• Create Spark UDF to fetch variable definitions:

  from colectica import Colectica
  from pyspark.sql.functions import udf
  
  client = Colectica(api_key="key")
  @udf("string")
  def get_variable_def(variable_name):
      return client.get_variable("census_2023", variable_name).definition

• Build script to auto-link Parquet files to Colectica metadata IDs.

Expected Outcome:
Spark queries auto-enriched with statistical metadata. No new platforms.

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Section 3: Implement Federated Queries

Context:
Users need unified access to databases, Parquet, and APIs without ingestion.

Todo:

• Create Spark notebook template with pre-configured connections:

  spark = SparkSession.builder \
      .appName("FederatedQuery") \
      .config("spark.jars", "/jdbc/jars") \
      .getOrCreate()
  
  # Query PostgreSQL via JDBC
  df_db = spark.read.format("jdbc") \
      .option("url", "jdbc:postgresql://host/db") \
      .option("dbtable", "sales") \
      .load()
  
  # Read Parquet
  df_parquet = spark.read.parquet("s3://bucket/data.parquet")
  
  # Join with metadata
  result = df_db.join(df_parquet, "id") \
      .withColumn("income_def", get_variable_def("income"))

• Test with 3 priority sources (e.g., PostgreSQL + S3 Parquet + API).

Expected Outcome:
Users query multiple sources without ingestion via Spark notebooks. Handles datasets too large for pandas.

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Section 4: Optimize Parquet with Iceberg

Context:
Existing Parquet files need schema evolution and ACID compliance without migration.

Todo:

• Add Iceberg Spark dependencies to environment.
• Convert Parquet to Iceberg format (zero data copy):

  df = spark.read.parquet("old_data.parquet")
  df.writeTo("catalog.db.new_data").createOrReplace()

• Test schema evolution: Add columns without rewriting data.

Expected Outcome:
Existing Parquet gains ACID compliance, time travel, and schema evolution. No data movement.

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Section 5: Orchestrate Workflows

Context:
Kubeflow must automate metadata enrichment and query optimization.

Todo:

• Create Kubeflow pipeline with:
  1. Spark job to link Colectica metadata to Parquet.
  2. Spark job to optimize Parquet with Iceberg.
  3. Notebook template for federated queries.
• Deploy template via JupyterHub with pre-configured Spark session.

Expected Outcome:
Kubeflow automates end-to-end workflow. Users start analysis with 1-click notebook.

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Overall Expected Outcome

• Tech Debt: 90% reuse (Kubernetes, Kubeflow, ODBC, Colectica, Parquet).
• Timeline: 4 weeks (Spark: 1 week, Colectica: 1 week, Features: 2 weeks).
• User Impact:
  • Unified data access matching Fabric's ease (virtualized queries + statistical context).
  • 50% reduction in data prep time.
  • ACID-compliant Parquet with schema evolution.
• Cost: Minimal (uses existing cluster + open-source components).

Why This Works: The Lean Data Virtualization Strategy

We're solving the core pain point – Fabric's unified data access – by layering lightweight capabilities on top of existing tools. Instead of rebuilding our stack, we're adding:

1. Spark on Kubernetes (1 Helm install) for scalable queries
2. Colectica metadata (Python SDK) for statistical context
3. Iceberg (zero-copy Parquet upgrade) for schema evolution

This creates a "virtual data lake" where users query data where it lives – no ingestion, no migration, no new infrastructure.

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How It Compares to Fabric

Capability	Fabric	Our Stack
Data Access	Requires ingestion into OneLake	Query in-place (DBs + Parquet + APIs)
Statistical Context	Basic lineage	Rich DDI metadata via Colectica
Cost	High (licensing + storage + egress)	Near-zero (open-source + existing infra)
Flexibility	Microsoft ecosystem lock-in	Open standards (Parquet, SQL, Python)
Setup Time	Months (migration + training)	Weeks (incremental rollout)

Key Advantage: We match Fabric's ease of access without forcing data migration – while exceeding it in statistical governance.

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Why a Small Team Can Manage This

1. We Reuse 90% of Existing Tools

• ✅ Kubernetes: Already runs Kubeflow/JupyterLab
• ✅ Colectica: Already manages metadata
• ✅ Parquet: Already our storage format
• ✅ ODBC: Already connects to databases

2. Each Phase Adds Minimal Complexity

Phase	Work	Team Effort	Risk
1. Spark on K8s	Helm install + 1 test notebook	1 dev, 3 days	Low
2. Federated Queries	1 SQL template + 2 data sources	1 dev, 2 days	Low
3. Colectica/Iceberg	Optional UDF + Parquet upgrade	1 dev, 4 days	Medium

3. Built-In Risk Controls

• Rollback Path: helm uninstall spark-operator reverts Phase 1
• Resource Limits: Spark runs in constrained K8s namespaces
• Phased Value: Each stage delivers immediate user value before adding complexity

4. Skills Transfer Easily

• Kubernetes knowledge → Spark Operator management
• Python skills → Colectica SDK + Spark UDFs
• SQL knowledge → Federated query templates

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The Outcome: Competitive Edge, Minimal Overhead

• For Users:
  "I query PostgreSQL + S3 Parquet in one notebook, with variable definitions auto-attached from Colectica – just like Fabric, but faster and cheaper."

• For the Team:
  "We added virtualization in 4 weeks using tools we already know. No new servers, no licensing fights, no migration nightmares."

• For the Business:
  "We matched Fabric's capabilities at 10% of the cost, while keeping our open-source flexibility."

This works because we turned constraints into advantages:

• Small team → Focused, incremental changes
• Existing tools → Zero infrastructure bloat
• Open standards → No vendor lock-in

Fabric's strength is integration; ours is lean integration without compromise.

graph TD
    %% Central Platform
    Platform[Lean Data Virtualization Platform]
    
    %% Implementation Components (arranged in a grid)
    subgraph Implementation[ ]
        direction LR
        S1[Section 1<br/>Spark on K8s]
        S2[Section 2<br/>Colectica Integration]
        S3[Section 3<br/>Federated Queries]
        S4[Section 4<br/>Iceberg Optimization]
        S5[Section 5<br/>Workflow Orchestration]
    end
    
    %% Business Outcomes
    subgraph Outcomes[ ]
        direction TB
        O1[Unified Data Access<br/>No Data Movement]
        O2[50% Faster Data Preparation]
        O3[Advanced Data Compliance<br/>ACID & Schema Evolution]
    end
    
    %% Connections
    Platform --> S1
    Platform --> S2
    Platform --> S3
    Platform --> S4
    Platform --> S5
    
    S1 --> O1
    S2 --> O1
    S3 --> O2
    S4 --> O3
    S5 --> O2
    
    %% Styling
    classDef platform fill:#4a86e8,stroke:#2a5db0,color:#fff,stroke-width:2px
    classDef section fill:#e8f0fe,stroke:#4a86e8
    classDef outcome fill:#d4edda,stroke:#28a745
    
    class Platform platform
    class S1,S2,S3,S4,S5 section
    class O1,O2,O3 outcome

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