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What is Pipeline Enterprise?

Pipeline Enterprise provides a comprehensive and intuitive solution that streamlines data collection along pipeline rights of way. By replacing the outdated practice of manual data entry into spreadsheets, this advanced system enables inspectors to directly input information, which is then effortlessly incorporated into a centralized reporting framework. Its flexibility allows it to be utilized in remote construction sites, making it suitable for all pipeline construction projects. The platform effectively mitigates issues related to data inconsistencies in quality control, construction progression, workforce management, and overall project effectiveness. Notable features include real-time tracking of progress, improved accountability for inspections, substantial time and cost savings, standardized processes and quality assurance, essential performance metrics, and compliance with regulatory requirements. Built on a solid foundation of proven engineering principles, Pipeline Enterprise software integrates global best practices within the industry. Its infrastructure harnesses state-of-the-art mobile, GPS, GIS, DataStore, and cloud technologies, ensuring it remains accessible and efficient across diverse environments, thus proving to be an essential asset for the sector. Moreover, its ability to adapt to changing project demands emphasizes its crucial role in contemporary pipeline management and enhances its utility in various operational scenarios.

What is IBM Databand?

Monitor the health of your data and the efficiency of your pipelines diligently. Gain thorough visibility into your data flows by leveraging cloud-native tools like Apache Airflow, Apache Spark, Snowflake, BigQuery, and Kubernetes. This observability solution is tailored specifically for Data Engineers. As data engineering challenges grow due to heightened expectations from business stakeholders, Databand provides a valuable resource to help you manage these demands effectively. With the surge in the number of pipelines, the complexity of data infrastructure has also risen significantly. Data engineers are now faced with navigating more sophisticated systems than ever while striving for faster deployment cycles. This landscape makes it increasingly challenging to identify the root causes of process failures, delays, and the effects of changes on data quality. As a result, data consumers frequently encounter frustrations stemming from inconsistent outputs, inadequate model performance, and sluggish data delivery. The absence of transparency regarding the provided data and the sources of errors perpetuates a cycle of mistrust. Moreover, pipeline logs, error messages, and data quality indicators are frequently collected and stored in distinct silos, which further complicates troubleshooting efforts. To effectively tackle these challenges, adopting a cohesive observability strategy is crucial for building trust and enhancing the overall performance of data operations, ultimately leading to better outcomes for all stakeholders involved.

Media

Media

Integrations Supported

Amazon EMR
Amazon S3
Apache Airflow
Azkaban
Databricks Data Intelligence Platform
Delta Lake
Docker
Google Cloud BigQuery
Google Cloud Composer
Google Cloud Dataproc
Google Cloud Storage
Kubernetes
MLflow
Microsoft Excel
MySQL
Oracle Cloud Infrastructure
PostgreSQL
Python
SQL Server
Snowflake

Integrations Supported

Amazon EMR
Amazon S3
Apache Airflow
Azkaban
Databricks Data Intelligence Platform
Delta Lake
Docker
Google Cloud BigQuery
Google Cloud Composer
Google Cloud Dataproc
Google Cloud Storage
Kubernetes
MLflow
Microsoft Excel
MySQL
Oracle Cloud Infrastructure
PostgreSQL
Python
SQL Server
Snowflake

API Availability

Has API

API Availability

Has API

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Pricing Information

Pricing not provided.
Free Trial Offered?
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Metegrity

Date Founded

1992

Company Location

Canada

Company Website

metegrity.com/pipeline-enterprise

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/products/databand

Categories and Features

Categories and Features

Data Lineage

Database Change Impact Analysis
Filter Lineage Links
Implicit Connection Discovery
Lineage Object Filtering
Object Lineage Tracing
Point-in-Time Visibility
User/Client/Target Connection Visibility
Visual & Text Lineage View

Data Preparation

Collaboration Tools
Data Access
Data Blending
Data Cleansing
Data Governance
Data Mashup
Data Modeling
Data Transformation
Machine Learning
Visual User Interface

Data Quality

Address Validation
Data Deduplication
Data Discovery
Data Profililng
Master Data Management
Match & Merge
Metadata Management

Data Visualization

Analytics
Content Management
Dashboard Creation
Filtered Views
OLAP
Relational Display
Simulation Models
Visual Discovery

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