Schlumberger
Scaling Data Analytics in Oil & Gas: Overcoming Schlumberger's Big Data Integration Challenges
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1926 - Texas, USA
Energy and Utilities
10,001+ employees
Overview
Schlumberger (SLB), the world’s largest offshore drilling company, was facing significant challenges in processing and analyzing the immense volumes of data generated by its operations. These challenges stemmed from the integration of diverse data sources, the ingestion of rapidly increasing data volumes, and the management of complex datasets. To address these issues and harness the full potential of their data, SLB partnered with Folio3 to deploy a robust Big Data Analytics platform, designed to transform their operations through real-time, actionable insights.
The Challenge: Managing and Analyzing Massive Data Volumes
SLB needed a solution that could manage and integrate these massive data volumes while ensuring real-time analytics and insights.
As SLB’s operations expanded, so did the volume and complexity of their data. Traditional data processing methods became inadequate, leading to several critical challenges:
Scalability Issues: Existing systems were unable to scale with the growing data volumes, resulting in processing bottlenecks and delays.
Data Integration: SLB’s data came from multiple sources, including legacy systems, each with different formats and structures, making integration a significant challenge.
Data Consistency: Variations in data formatting across platforms caused inconsistencies, hampering the accuracy of analytics.
Operational Inefficiencies: The lack of a streamlined continuous integration and deployment (CI/CD) process led to prolonged downtimes and operational inefficiencies.
The Solution: Cloud-Based Advanced Data Processing & Analytics
Folio3 conducted a thorough assessment of SLB’s data infrastructure and identified the need for a scalable, cloud-based advanced data analytics system. The solution was implemented using Microsoft Azure, leveraging its powerful tools to meet SLB’s specific needs.
Data Ingestion with Azure Data Factory
Data from multiple source systems, including SAP, SQL Server, Hybris, and Google Analytics, is ingested using Azure Data Factory. This ensures a continuous and reliable flow of data into the analytics platform.
Daily Snapshots for Audits and Historical Analysis
To ensure data integrity and traceability, daily snapshots of ADLS Hot Tier datasets are maintained in the Cold Tier, enabling thorough audits and historical analysis.
Raw Data Storage in Azure Data Lake Storage (ADLS) (Cold Tier)
Raw data is stored in Azure Data Lake Storage (Cold Tier), where it undergoes minor transformations to ensure consistency, such as standardizing decimal places, phone numbers, and dates.
AI/ML Transformations with Azure Databricks
Advanced AI/ML models are applied to both raw and analytical datasets using Azure Databricks, providing predictive insights and driving data-driven decisions.
Transformation with Azure Databricks
Data in the ADLS (Cold Tier) is transformed into analytical datasets using Azure Databricks. These datasets are then stored in ADLS (Hot Tier) for real-time reporting and analytics.
User-Friendly Dataset with Azure Analysis Services
A curated, user-friendly dataset is made available through Azure Analysis Services, allowing users to create custom reports with their preferred tools.
Power BI Dashboards
Power BI dashboards are built on top of the analytical datasets in ADLS (Hot Tier), offering real-time, customizable insights for end-users.
Advanced Reporting
Folio3 also developed advanced dashboards and reports, enabling comprehensive analysis through various filters. Report processing times were optimized, reducing execution times from minutes to milliseconds. Daily leaderboards were also implemented, providing up-to-date performance metrics.
Technologies Involved In This Case
Microsoft Azure
Azure Data Factory
SAP
SQL Server
Google Analytics
Pandas
Azure Data Lake Storage (ADLS) (Cold & Hot Tiers)
Azure Databricks
Azure Analysis Services
Power BI
Results & Achievements
Seamless Data Integration
Successfully integrated data from multiple sources, ensuring consistency and reliability.
Enhanced Data Consistency
Achieved high data consistency through automated standardization processes.
Real-Time Analytics
Enabled real-time analytics, allowing for quick, data-driven decisions.
Reduced Report Processing Times
Optimized report generation, reducing times from minutes to milliseconds.
Daily Data Snapshots
Maintained daily snapshots for robust auditing and historical analysis.
Advanced AI/ML Capabilities
Applied sophisticated AI/ML models for predictive insights.
Customizable Dashboards
Empowered users with customizable, real-time dashboards.
Scalable, Future-Proof Architecture
Developed a platform that seamlessly integrates with any data ingestion system, ensuring future-proofing and operational efficiency.
Operational Efficiency with CI/CD
Implemented CI/CD pipelines, reducing deployment times and downtime.
Significant Cost Savings
Achieved substantial cost savings through optimized infrastructure design, reducing operational expenses while scaling analytical capabilities.