Paxata - Change the data from chaos to clarity - data analysis using DataRobot Data Prep.

Devopsbay helped a multinational manufacturing company on a project to speed up the data preparation process by 70% by implementing DataRobot Data Prep. The project focused on automating the cleaning and transformation of data from multiple sources, significantly reducing the time required to prepare data for analysis.

  • Devops
  • MLOps
  • Infrastructure
  • Helm
  • Terraform
  • Java
  • +6
    Devops
    MLOps
    Infrastructure
    Helm
    Terraform
    Java

about the project

DataRobot Data Prep (formerly known as Paxata) is an advanced data preparation and management tool.

This solution allows organisations to efficiently collect, explore and prepare data from multiple sources for machine analysis. The three main solutions offered by Paxata are:

  • Data cleansing automation - the system automatically detects and fixes errors, inconsistencies and gaps in data, significantly speeding up the data preparation process.
  • Advanced integration of data sources - the platform enables data from different systems (including databases, local files, the cloud) to be combined into one coherent set.
  • Shared and reused workflows - users can save and share both the data and the steps used to prepare it, making teamwork more efficient.

the challenge

Streamline work on large data sets.

technologies

JDBC

Kerberos

Apache Hive

Apache Impala

Azure Data Lake Storage

Databricks

Kubernetes

Helm

NGINX

Bash

Kustomize

  • JDBC (Java Database Connectivity)

    A framework for connecting to different databases, used to import and export data from multiple sources.

  • Kerberos

    An authentication system used for secure access to Hadoop clusters and Big Data services.

  • Apache Hive

    A data processing framework used to analyze large data sets in a Hadoop environment.

  • Apache Impala

    SQL query engine used for fast data processing in Hadoop clusters.

  • Azure Data Lake Storage

    A data storage solution used in integration with Microsoft Azure services.

  • Databricks

    A data processing platform used for advanced analytics and machine learning.

  • Interactive Mode

    Paxat's own solution for working with real-time data as it is loaded.

  • Project Flows

    A data preparation automation system for creating repeatable workflows.

  • Kubernetes

    A container orchestration platform used to manage and scale Paxata microservices in a production environment.

  • Helm

    Kubernetes package manager used to define, install and update Paxata applications.

  • NGINX

    Proxy server used as an access layer and load balancer for applications.

  • Bash Scripting

    Scripts that automate deployment and environment management processes.

  • Kustomize

    Kubernetes manifest customization tool used to manage different environments.

Results

Enhanced platform performance

Devopsbay's support has optimized the platform's overall performance, enabling faster data processing and a more robust, reliable system for large-scale data operations.

Automated error detection

The system now automatically detects and corrects data errors, with standardized formats that ensure data consistency across sources, minimizing manual intervention and errors.

Improved team collaboration

With shared workflows and reusable processes, teams can collaborate seamlessly on data preparation and analysis, leading to faster insights and improved efficiency across departments.

Enhanced platform performance

Devopsbay's support has optimized the platform's overall performance, enabling faster data processing and a more robust, reliable system for large-scale data operations.

Benefits

  • Data Integration

    Effective integration of multiple data sources into one complete system

  • System Reliability

    Ensure high system availability by using NGINX

  • Error Reduction

    Elimination of errors resulting from manual data processing

  • Team Collaboration

    Improving collaboration between analytical teams

  • Time Efficiency

    70% reduction in the time needed to prepare data for analysis

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