In this series, we demonstrate how we have applied our solutions to a variety of different projects in over twenty years of business. Last time, we went over a few basics as well as general data solutions. Now, here in part 2, we will show how we have applied solutions in data aggregation to a variety of projects. Like before, we will list out the solutions themselves and projects that fall under each category. Furthermore, we will also address the challenges involved with these solutions.
DATA AGGREGATION SOLUTIONS
Data helps understand “What happened?” Whether it’s raw feeds, simple datafiles, published datasets, logs, content, we provide a broad series of Integration and Interoperable data platform services: Data. By definition, data aggregation is the compiling of information from databases with intent to prepare combined datasets for data processing. The United States Geological Survey explains that, “when data are well documented, you know how and where to look for information and the results you return will be what you expect.” As mentioned before, it helps you understand the what and the why (What happened?). This is because data aggregation solutions focus on the documentation and compliation of data for processing. Hence the variety of solutions below. Like before, we will write out the solutions and challenges, and then link you to some examples.
- Pipelines (ELT, ETL, Serverless, COTS) – Hydrography Dataset ETL, LiDAR ETL, Survey Tool ETL to CARTO, ETL form creation, AWS Serverless Pipeline, COTS Technology for HealthCare
- Standards-based Transformations (Transformations, Formats, Encoding)
- Real-Time Data Stream Hubs
- Data Microservices – Integration Patterns for Microservices
- Data services (WMS, WFS, WCS, REST/XML Web Service) – Health Insurance Exchanges, OGC Web Map Profiles Based on XML, Publishing Data in an XML Format
- Database Connectors – Configuring Database and REST Connectors for Voice Skills
- Open Data Catalogs Metadata Inventory – Catalog Support
- Data Asset Registry & Search (Transformations, Datasets, Systems, Apps, Web Services, Feature, Layers, Metadata, Field Maps, Containers, algorithms, ontologies, scripts, code repo’s, reports)
Challenges to Address
- Data Aggregation, Ingestion and creation solutions lean serverless, highly configurable, ELT over ETL
- Data Orchestration Services look to in-line perform data standards validation, automate workflows, proactively detect schema and data anomalies, and automate metadata insights
- Data Management established metadata prioritization and analytics, catalog, authoritative data management, discovery, and archive approaches.
- Automate on-going Data integration to support information and knowledge solutions
- Streamlining processes and data by consolidating systems and technology
- Wrangling and extracting, transforming and loading (ETL) data to make it more useable and consumable
- Lower your disproportionate high investment in aging data production and loading systems
- Address data infrastruct inefficiencies: aging expensive ‘iron servers’/hardware, unmanaged or oversize cloud instances, and modernize technologies.
- Address and Govern Data maturity
- Integrate governance planning with metadata repositories connecting goals, metrics, use cases, data assets, systems, and technologies to support prioritization and complexity.
- Maximize full value from sensor investment by making higher access and available to analytics efforts
See You in Part 3
We hope you enjoyed another quick trip through our solutions. Also, we hope you enjoyed few examples of how we apply them as a company. We will see you in part 3, where we will discuss information platform solutions.