informatica-analyzer 0.1.0

Informatica PowerCenter XML workflow analyzer

informatica-analyzer

informatica-analyzer is a lightweight Python library and command-line tool for analyzing Informatica PowerCenter XML workflow exports.

It extracts workflow, session, mapping, source, target, lookup, Source Qualifier, SQL, truncate, connection, and flat-file metadata from Informatica XML files. The extracted metadata can be written as JSON and converted into CSV reports for auditing, migration analysis, documentation, and downstream automation.

---

Features

• Analyze Informatica PowerCenter XML export files
• Extract workflows, sessions, and mappings
• Identify source, target, and lookup objects
• Extract Source Qualifier SQL queries
• Extract Source Qualifier filters and user-defined joins
• Extract pre-SQL and post-SQL statements
• Detect target truncate settings
• Capture database and connection metadata
• Capture flat-file and reject-file metadata where available
• Generate structured JSON output
• Generate CSV reports
• Use as both a CLI tool and a Python library
• Extend with custom validation checks

---

Installation

Install from PyPI:

pip install informatica-analyzer

Verify the installation:

informatica-analyzer --help

---

Requirements

• Python 3.10 or newer
• Informatica PowerCenter XML export files

The package is intentionally lightweight and does not require a database connection.

---

CLI Usage

The main CLI command is:

informatica-analyzer

Analyze XML files and generate JSON and CSV output

informatica-analyzer \
  --xml-glob "inputs/*.xml" \
  --json-out-dir outputs/json \
  --csv-out-dir outputs

This command:

1. Reads all XML files matching inputs/*.xml
2. Writes one JSON file per XML file into outputs/json
3. Generates CSV reports in outputs

---

Generate CSV reports from existing JSON files

informatica-analyzer \
  --json-glob "outputs/json/*.json" \
  --csv-out-dir outputs

Use this when XML extraction has already been done and you only want to regenerate CSV reports.

---

Analyze XML and combine with existing JSON

informatica-analyzer \
  --xml-glob "inputs/*.xml" \
  --json-glob "outputs/json/*.json" \
  --json-out-dir outputs/json \
  --csv-out-dir outputs

This is useful when you want to process new XML files and include already-generated JSON files in the final CSV reports.

---

Generate compact JSON

informatica-analyzer \
  --xml-glob "inputs/*.xml" \
  --json-out-dir outputs/json \
  --compact-json

By default, JSON output is pretty-printed. Use --compact-json to reduce file size.

---

Explode detail rows

informatica-analyzer \
  --xml-glob "inputs/*.xml" \
  --json-out-dir outputs/json \
  --csv-out-dir outputs \
  --explode-details

By default, the detailed CSV report groups multiple source, target, lookup, and query values into one row per workflow-mapping pair.

With --explode-details, the report creates expanded rows for detailed spreadsheet analysis.

---

Change log level

informatica-analyzer \
  --xml-glob "inputs/*.xml" \
  --json-out-dir outputs/json \
  --csv-out-dir outputs \
  --log-level INFO

Supported log levels:

DEBUG
INFO
WARNING
ERROR

---

Python Library Usage

You can also use informatica-analyzer directly from Python.

Analyze one XML file

from informatica_analyzer import analyze_xml

metrics = analyze_xml("inputs/workflow_export.xml")

print(metrics["summary"])
print(metrics["workflows"])

---

Write metrics JSON for one XML file

from informatica_analyzer import write_metrics_json

json_path = write_metrics_json(
    "inputs/workflow_export.xml",
    "outputs/json/workflow_export.json",
)

print(json_path)

---

Analyze multiple XML files

from informatica_analyzer import analyze_xml_files

result = analyze_xml_files(
    [
        "inputs/workflow_export_1.xml",
        "inputs/workflow_export_2.xml",
    ],
    json_out_dir="outputs/json",
    csv_out_dir="outputs",
)

print(result["json_paths"])
print(result["csv_paths"])

---

Generate JSON only

from informatica_analyzer import analyze_xml_files

result = analyze_xml_files(
    ["inputs/workflow_export.xml"],
    json_out_dir="outputs/json",
    csv_out_dir=None,
)

print(result["json_paths"])

---

Build CSV rows in memory

from informatica_analyzer import build_csv_rows_from_json

rows = build_csv_rows_from_json(
    ["outputs/json/workflow_export.json"]
)

workflow_mapping_counts = rows["workflow_mapping_counts"]
workflow_mapping_details = rows["workflow_mapping_details"]
workflow_mapping_count = rows["workflow_mapping_count"]

print(workflow_mapping_details[:5])

---

Use the extractor directly

from pathlib import Path
from informatica_analyzer import InformaticaXmlMetricsExtractor

extractor = InformaticaXmlMetricsExtractor(Path("inputs/workflow_export.xml"))
metrics = extractor.parse()

print(metrics["details_grouped_by_mapping_name"].keys())

---

Output Files

A typical run produces:

outputs/
├── json/
│   ├── workflow_export_1.json
│   └── workflow_export_2.json
├── workflow_mapping_counts.csv
├── workflow_mapping_details.csv
└── workflow_mapping_count.csv

---

JSON Output

Each XML file produces one JSON file.

Example top-level structure:

{
  "input_file": "inputs/workflow_export.xml",
  "summary": {},
  "workflows": [],
  "details_grouped_by_mapping_name": {},
  "mappings": [],
  "lists": {}
}

Summary

The summary block contains high-level counts:

{
  "number_of_workflows": 1,
  "number_of_mappings_in_workflows": 3,
  "number_of_sources_in_those_mappings": 8,
  "number_of_targets_in_those_mappings": 4,
  "number_of_lookups_in_those_mappings": 2
}

Workflows

The workflows block contains workflow-level metadata:

[
  {
    "name": "wf_customer_load",
    "number_of_mappings": 2,
    "mapping_names": [
      "m_customer_stage",
      "m_customer_target"
    ],
    "sessions": [
      {
        "workflow_name": "wf_customer_load",
        "session_name": "s_customer_stage",
        "mapping_name": "m_customer_stage"
      }
    ]
  }
]

Mapping details

The details_grouped_by_mapping_name block contains mapping-level metadata:

{
  "m_customer_stage": {
    "mapping_name": "m_customer_stage",
    "counts": {
      "sources": 1,
      "targets": 1,
      "lookups": 0,
      "source_qualifier_sql_queries": 1,
      "source_qualifier_filters": 0,
      "source_qualifier_user_defined_joins": 0,
      "pre_sql": 0,
      "post_sql": 0,
      "target_truncate_flags": 1
    },
    "source_table_names": ["CUSTOMER_SRC"],
    "target_table_names": ["CUSTOMER_STG"],
    "lookup_table_names": [],
    "source_qualifier": {
      "sql_queries": [],
      "source_filters": [],
      "user_defined_joins": [],
      "details": []
    },
    "pre_sql": [],
    "post_sql": [],
    "target_truncate": []
  }
}

---

CSV Reports

workflow_mapping_counts.csv

One row per workflow-mapping pair.

Column	Description
wf_name	Workflow name
mapping_name	Mapping name
n_source	Number of source objects
n_target	Number of target objects
n_lookup	Number of lookup objects

---

workflow_mapping_details.csv

Detailed workflow and mapping metadata.

Column	Description
wf_name	Workflow name
mapping_name	Mapping name
s_database	Source database or connection
s_table	Source table
s_flat_file_name	Source flat-file name
s_flat_file_path	Source flat-file path
t_database	Target database or connection
t_table	Target table
t_flat_file_name	Target flat-file name
t_flat_file_path	Target flat-file path
t_reject_file_name	Target reject file name
t_reject_file_path	Target reject file path
l_table	Lookup table
sq_query	Source Qualifier query

---

workflow_mapping_count.csv

One row per workflow.

Column	Description
workflow_name	Workflow name
mapping_count	Number of unique mappings used by the workflow

---

Flat-File Metadata

For mappings that use flat files, the analyzer captures flat-file metadata where available in the Informatica session metadata.

Captured fields include:

• flat_file_directory
• flat_file_name
• flat_file_path
• reject_file_directory
• reject_file_name
• reject_file_path

Example:

{
  "mapping_name": "m_customer_file_load",
  "instance_name": "CUSTOMER_FILE_SRC",
  "table_name": "CUSTOMER_FILE_SRC",
  "object_type": "source",
  "flat_file_directory": "/data/inbound/customer",
  "flat_file_name": "customer.csv",
  "flat_file_path": "/data/inbound/customer/customer.csv"
}

---

Checks

Checks are optional validation utilities that can be built on top of extracted metadata.

The package includes basic file-data checks.

Check whether a file exists and has data

from pathlib import Path
from informatica_analyzer.checks.file_data import check_file_has_data

result = check_file_has_data(Path("/data/inbound/customer/customer.csv"))

print(result.exists)
print(result.has_data)
print(result.size_bytes)
print(result.reason)

Possible result:

FileDataCheckResult(
    path="/data/inbound/customer/customer.csv",
    exists=True,
    has_data=True,
    size_bytes=2048,
    reason=None,
)

---

Check many files

from pathlib import Path
from informatica_analyzer.checks.file_data import check_files_have_data

paths = [
    Path("/data/inbound/customer/customer.csv"),
    Path("/data/inbound/order/order.csv"),
]

results = check_files_have_data(paths)

for result in results:
    print(result.path, result.has_data, result.reason)

---

Extending the Package

New validation or audit logic should be added under:

src/informatica_analyzer/checks/

For example:

checks/file_data.py
checks/source_target_files.py
checks/sql_rules.py
checks/connection_rules.py
checks/truncate_rules.py

The extractor should focus on reading Informatica XML metadata.

Checks should answer validation questions such as:

• Does the source file exist?
• Does the target file have data?
• Is truncate enabled?
• Is a connection allowed?
• Does a SQL query contain a prohibited pattern?

This keeps extraction and validation separate.

---

Development

Clone the repository and install in editable mode:

git clone <your-repository-url>
cd informatica-analyzer
python -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[dev]"

On Windows PowerShell:

git clone <your-repository-url>
cd informatica-analyzer
python -m venv .venv
.venv\Scripts\Activate.ps1
python -m pip install -e ".[dev]"

---

Run Tests

python -m pytest -q

Run one test file:

python -m pytest tests/test_xml_utils.py -q

---

Project Structure

informatica-analyzer/
├── pyproject.toml
├── README.md
├── src/
│   └── informatica_analyzer/
│       ├── __init__.py
│       ├── api.py
│       ├── cli.py
│       ├── extractor.py
│       ├── models.py
│       ├── pipeline.py
│       ├── xml_utils.py
│       ├── checks/
│       │   ├── __init__.py
│       │   └── file_data.py
│       └── reporting/
│           ├── __init__.py
│           └── csv_reports.py
└── tests/
    └── test_xml_utils.py

---

Troubleshooting

informatica-analyzer: command not found

Install the package in the current Python environment:

python -m pip install -e .

Then verify:

informatica-analyzer --help

---

ModuleNotFoundError: No module named 'informatica_analyzer'

You may be using a different Python environment.

Check:

which python
python -m pip --version

Then reinstall:

python -m pip install -e .

---

pytest: command not found

Install development dependencies:

python -m pip install -e ".[dev]"

Or install pytest directly:

python -m pip install pytest

Then run:

python -m pytest -q

---

Empty CSV reports

Possible causes:

• The XML export does not contain workflow metadata
• Sessions are not linked to mappings
• The XML export contains partial repository metadata
• The wrong XML files were passed to --xml-glob

Start by inspecting the generated JSON file:

cat outputs/json/your_file.json

Check these keys:

{
  "summary": {},
  "workflows": [],
  "details_grouped_by_mapping_name": {}
}

---

License

This project is licensed under the MIT License.

See the LICENSE file for details.