MSDN Documentation

Data Analytics Service

The Data Analytics Service provides a comprehensive suite of tools and capabilities for ingesting, processing, analyzing, and visualizing large datasets. It empowers developers and data scientists to derive actionable insights from their data, enabling better decision-making and driving business innovation.

Key Features

  • Scalable Data Ingestion: Support for various data sources including databases, streams, and files.
  • Powerful Data Processing: Utilize batch and stream processing engines for real-time and historical analysis.
  • Advanced Analytics: Built-in libraries and integrations for statistical analysis, machine learning, and data mining.
  • Interactive Visualization: Tools to create dynamic dashboards and reports for easy data exploration.
  • Secure and Compliant: Robust security features and adherence to industry compliance standards.

Core Components

The Data Analytics Service is composed of several interconnected components:

  • Data Lake: A central repository for storing raw and processed data in its native format.
  • Processing Engine: Supports distributed processing frameworks like Apache Spark and Flink.
  • Query Engine: Enables interactive SQL-based querying over stored data.
  • Visualization Layer: Integrates with popular BI tools and provides a native dashboarding interface.
  • Machine Learning Toolkit: Seamless integration with our Machine Learning Service for predictive modeling.

API Endpoints

Here are some of the key API endpoints for interacting with the Data Analytics Service:

HTTP Method Endpoint Description
POST /data-analytics/v1/jobs Submit a new data processing job.
GET /data-analytics/v1/jobs/{jobId} Retrieve details of a specific data processing job.
GET /data-analytics/v1/datasets List available datasets.
POST /data-analytics/v1/datasets Register a new dataset.
GET /data-analytics/v1/dashboards Retrieve a list of available dashboards.
POST /data-analytics/v1/dashboards Create a new dashboard.

Getting Started

To start using the Data Analytics Service, follow these steps:

  1. Authenticate: Obtain your API credentials from the MSDN Developer Portal.
  2. Ingest Data: Use the Data Ingestion API or upload files directly through the portal to populate your Data Lake.
  3. Create a Job: Define and submit a data processing job using the /data-analytics/v1/jobs endpoint.
  4. Analyze Results: Query your processed data or visualize insights through the dashboarding tools.

Example: Submitting a Processing Job

Below is an example of how to submit a simple Spark job to process a CSV file:


{
  "jobName": "csv-processing-job",
  "jobType": "spark",
  "scriptPath": "s3://my-data-lake/scripts/process_csv.py",
  "inputDataset": "raw_sales_data",
  "outputDataset": "processed_sales_data",
  "parameters": {
    "output_format": "parquet"
  }
}

Further Reading