Data Platform Management
Smart solutions to unlock your enterprise data's full potential with leading partners
The rise of data, analytics, and AI offers organizations a unique opportunity to drive growth. However, challenges such as siloed data, poor quality, and insufficient governance can limit access to valuable insights, hinder decision-making, and introduce compliance risks. To fully leverage AI and analytics, businesses must modernize legacy systems, enabling agility, real-time decision-making, and accelerated growth. With increasing data volumes, scalable solutions are crucial to staying ahead with systems that evolve to meet future demands.
At Torry Harris, we offer a comprehensive range of services, including Data Transformation & Integration, Data Migration, Data Virtualization, Data Governance, Data Insights and AI, Data Architecture, and AI-Powered Data Marketplace. With close collaboration with our partner platforms, we ensure businesses gain accurate, real-time insights through automated quality control, modernized platforms, and strong governance frameworks.
Our Data Platform Management Partners
AWS
- Boost cloud database performance with Amazon Aurora, with MySQL and PostgreSQL compatibility.
- Scale effortlessly with Amazon DynamoDB, a serverless NoSQL service that automatically adapts to demand with zero downtime.
- Automate database management tasks with Amazon RDS, reducing complexity and cost for provisioning, scaling, and patching.
- Drive insights with Amazon QuickSight, a business intelligence platform offering interactive dashboards and natural language queries.
- Simplify ETL workflows with AWS Glue and unlock high-performance analytics with Amazon Redshift.
- Streamline big data processing with Amazon EMR Serverless, running Apache Spark, Hive, and Presto without cluster management.
- Process large-scale data streams in real-time with Amazon Kinesis Data Streams, capturing data from various sources.
Microsoft Azure
- Build and deploy scalable data, analytics, and AI solutions with Azure Databricks.
- Store and serve files, stream media, and perform backup and recovery with Azure Blob Storage.
- Analyze large-scale data in any format with Azure Data Lake Storage for big data workloads.
- Automate ETL/ELT processes and integrate data effortlessly using Azure Data Factory, delivering insights with Azure Synapse.
- Unify data movement, processing, and reporting with Microsoft Fabric’s end-to-end analytics platform.
- Visualize and share data insights at scale with Power BI, fostering a data-driven culture alongside Azure Analytics.
Google Cloud
- Utilize Cloud Storage FUSE for scalable, affordable storage while maintaining filesystem compatibility.
- Speed up real-time decision-making with Dataflow’s fully managed, scalable ML pipelines.
- Modernize data lakes and run ETL at scale with Dataproc, integrating seamlessly with Google Cloud.
- Centralize siloed data and streamline integration with Cloud Data Fusion’s cloud-native platform.
- Orchestrate workflows with Cloud Composer, offering Apache Airflow’s portability and broad platform integration.
- Manage and govern data with Dataplex through intelligent profiling, quality assessment, and lineage tracking.
- Maximize insights with BigQuery, a fully managed, AI-ready analytics platform supporting multi-cloud environments.
- Achieve seamless scalability and performance optimization with Bigtable’s automated resource management.
- Gain actionable insights with Looker, providing real-time, governed data views across multiple clouds.
Ab Initio
- Centralize and catalog data assets with a searchable tool to enhance discovery and collaboration.
- Accelerate query performance at scale using Denodo’s Presto-based MPP engine combined with Smart Query Accelerator features.
- Optimize query execution with Denodo’s data virtualization platform, ensuring maximum efficiency across multiple data sources.
- Utilize intelligent caching and optimized data movement to improve query performance.
- Leverage embedded MPP for handling complex data challenges, from cloud analytics to federated queries.
- Unlock the full potential of your data with Denodo’s query acceleration capabilities.
Denodo Platform
- The Denodo Platform is the leading logical data management solution, built on data virtualization, enabling real-time data access, integration, and secure sharing with breakthrough cost-effectiveness.
- Centralize your data assets in a catalog for easy discovery, understanding, and access across lakes, clouds, and systems.
- Accelerate query performance at scale using Denodo’s Presto-based MPP engine combined with Smart Query Accelerator features.
- Optimize query execution with Denodo’s data virtualization platform, ensuring maximum efficiency across multiple data sources.
- Utilize intelligent caching and optimized data movement to improve query performance.
- Leverage embedded MPP for handling complex data challenges, from cloud analytics to federated queries.
- Unlock the full potential of your data with Denodo’s query acceleration capabilities.
The Databricks Data Intelligence Platform
- Leverage the Databricks Data Intelligence Platform, built on a unified Lakehouse foundation, for real-time data access, integration, and governance.
- Power your AI initiatives with the Data Intelligence Engine, which automatically optimizes performance and manages infrastructure based on your business’s unique data.
- Simplify data search and discovery with natural language processing.
- Accelerate data and application development with natural language assistance to write code, remediate errors, and find solutions.
- Ensure strong governance and security for AI and MLOps initiatives, maintaining data privacy and IP control across all AI projects.
- Build and deploy AI applications using both APIs (like OpenAI) and custom models, all within a secure, unified platform.
Boomi DataHub
- Ensure consistent, reliable, and accurate data with Boomi DataHub’s cloud-based master data synchronization service.
- Maintain up-to-date, validated golden records by identifying and quarantining low-quality data for review.
- Synchronize data across SaaS, on-premises, and hybrid environments using Boomi Integration and the DataHub connector.
- Enable multiple applications to access consistent, high-quality data by referencing golden records across your organization.
- Simplify data management by defining ideal records and deploying data models to ensure data quality standards are met.
Workato Data Management
- Streamline data orchestration with Workato’s flexible platform, designed for simplicity and hyper-automation.
- Break down data silos and ensure consistent, accurate data across systems with Workato’s Data Hub.
- Unify, standardize, and mobilize data across business domains to create a single source of truth on a scale.
- Leverage multidomain MDM and customer 360 capabilities to meet your data unification needs.
- Empower citizen builders to create robust data orchestrations without compromising on capability.
Snowflake
- Load data in real-time from files using Snowpipe, making it available to users within minutes through micro-batching.
- Store, process, and analyze data faster and more flexibly with Snowflake’s self-managed Data Cloud platform.
- Benefit from a completely new SQL query engine and architecture designed natively for the cloud, distinct from traditional database or big data technologies.
- Build data pipelines, ML models, apps, and perform other data processing tasks using Snowpark’s libraries and execution environments.
- Enable seamless integration and analytics with Snowflake’s cloud-native platform for enhanced data processing and management.
IBM
- Scale and manage data effortlessly with IBM Cloudant, a fully managed NoSQL database built for high traffic and large volumes of data.
- Accelerate decision-making, ensuring security with IBM Db2, an AI-powered database offering real-time analytics and multi-cloud support.
- Develop applications with IBM Informix using client APIs and 4GL development tools, optimized for performance and flexibility.
- Run applications, analytics, and AI workloads seamlessly across any cloud with IBM’s portfolio of relational and NoSQL databases.
- Leverage open-source ecosystems with IBM and Cloudera’s partnership to enable faster data and analytics at scale.
- Use MongoDB’s flexible document-based storage for efficient management of diverse data types across your applications.
Success Stories
CASE STUDY
Data virtualization – Tableau dashboard acceleration
CASE STUDY
Data as a Service (DaaS)
CASE STUDY
Databridge – energy supply client
CASE STUDY
Master data management – Telco client
CASE STUDY
Device data for network performance analysis
CASE STUDY
Data virtualization – Tableau dashboard acceleration
Problem statement
The client encountered two critical challenges that impeded their business intelligence
capabilities. The
data preparation process was highly manual, requiring extensive effort to extract,
transform, and
consolidate data from multiple disparate sources before it could be visualized in Tableau.
This
labor-intensive workflow led to prolonged BI project timelines, delaying insights and
slowing
decision-making. Concurrently, users faced significant performance issues with Tableau
dashboards,
characterized by high query latency and extended loading times, particularly for complex,
data-intensive
visualizations. These challenges not only created operational inefficiencies but also
hindered the client’s
ability to deliver real-time analytics and actionable insights, limiting their agility in
responding to
business needs.
Solution
- Implemented data virtualization to streamline and accelerate data preparation for Tableau visualizations.
- Enabled a self-service, DIY development model for BI teams, reducing dependency on manual processes.
- Applied Smart Query Acceleration techniques to enhance Tableau query performance and ensure seamless visualization, even with complex datasets.
Business benefits
- Achieved 50-80% reduction in BI project delivery timelines by removing data preparation bottlenecks.
- Ensured Tableau dashboards load within five seconds, regardless of data complexity.
- Improved decision-making boosted user satisfaction, and increased adoption of BI tools for real-time insights.
CASE STUDY
Data as a Service (DaaS)
Problem statement
The client wanted to make enterprise data readily accessible for consumption across
functions. Exposing
datasets as APIs required a time-consuming Java development process, which created
bottlenecks and slowed
data delivery to downstream applications. Additionally, the distributed access control setup
was overly
complex, making it difficult to enforce consistent security policies across diverse user
groups and data
layers. These challenges limited scalability, increased operational overheads, and posed
risks to data
security.
Solution
- Leveraged data virtualization to expose datasets as APIs out of the box
- Implemented a centralized role-based access control system to enforce granular data security at the view, column, and row levels.
Business benefits
- Achieved a 50-80% reduction in time required to expose datasets as APIs, enabling faster data delivery.
- Simplified governance and enhanced compliance through centralized, consistent, and granular access control mechanisms.
- Accelerated data accessibility while maintaining robust security standards.
CASE STUDY
Databridge – Energy supply client
Problem statement
The client faced a challenge in connecting corporate data residing on AWS to the Azure data
platform in near
real-time. This integration gap hindered the ability to deliver timely and accurate
recommendations,
limiting operational efficiency. The lack of seamless data flow between the two platforms
created delays in
data availability, affecting decision-making, predictive analytics, and the overall
performance of plant
operations. The organization needed a scalable, reliable solution to bridge this data gap
and enable
real-time insights for improved plant performance and operational agility.
Solution
- Dev Portal: Developed a single sign-on enabled web application for data owners to manage the publication and subscription of data objects between corporate big data platforms. The application was built using a microservices architecture to ensure scalability and API readiness.
- Event Mediation Hub: Implemented a Kafka platform as a service, enabling a publish-subscribe backbone for real-time data transport across the enterprise. This hub uses a Kafka Connect framework-based sink connector to push data to Azure Event Hub.
- Data Publisher: Deployed an intelligent ETL solution on the AWS stack to publish data objects from the Dev Portal to Kafka topics. This solution is agnostic to data objects and scalable to meet future publishing needs
Business benefits
- Improved plant performance and quality, leading to enhanced operational efficiency.
- Increased customer satisfaction through improved product quality and traceability.
- Reduced production costs by proactively identifying equipment issues and minimizing cycle times.
- Enabled continuous performance improvement in manufacturing processes.
- Empowered plant and supply chain managers, maintenance and quality engineers, and performance experts with data-driven insights to take informed actions.
CASE STUDY
Master data management – Telco client
Problem statement
The client challenge was inefficient master data management, resulting in data fragmentation
across systems,
delayed synchronization, and operational inefficiencies in sales, service, and maintenance.
This lack of a
unified data structure led to difficulties in maintaining accurate, up-to-date information,
hindering
effective decision-making and increasing the complexity of system integrations.
Solution
- Implemented a single centralized master data system for key business entities, including Business, Contact, Consent, and Sales Ownership.
- Developed and managed a golden record by integrating both external and internal data sources.
- Enabled seamless system synchronization with the Master Data Management (MDM) system, eliminating dependencies between systems.
- Adopted an API-first approach to integrate systems and applications, ensuring smooth data flow and accessibility.
- Established "Master Once, Reuse Many" framework, positioning MDM as the source of truth for any new applications requiring business data.
Business benefits
- Streamlined operations with 22 batch and real-time applications integrated into the MDM system.
- Processed over 5GB of data daily, ensuring up-to-date information across systems.
- Mastered 13 million records, providing a single, reliable source of truth.
- Generated over 8 aggregate reports daily, delivering actionable insights for various business streams.
CASE STUDY
Device data for network performance analysis
Problem statement
The client needed to aggregate and process data from multiple mobile sites and cells to
gauge analytics on
network performance, including capacity, energy efficiency, power throughput, and overall
performance
management. The fragmented data posed challenges in gaining unified insights and making
data-driven
decisions for optimizing network operations.
Solution
- Stored raw data in the Centralized Cell Network (CCN) system, with dimensions and facts built on Big Query within the Data Processing Network (DPN).
- Utilized the Formula Master to define KPIs and standardization rules for critical fields like frequency and decibel levels.
- Leveraged Looker and Vertex AI for advanced analytics and visualization, enabling deeper insights into network performance.
Business benefits
- Standardized and collated data from 20,000 mobile sites and 260,000 cells, covering key metrics such as signal frequency, uplink, and downlink performance, within Greenplum DB.
- Processed approximately 30GB of data daily, ensuring real-time insights and enabling better decision-making for network optimization.
