The Modern Data Warehouse
“Now everything produces data: every click on the internet, every package on your doorstep, every bag on the airplane, every step on your health tracker. It’s not only inexpensive to store – disk storage on premise or in the cloud is exponentially cheaper than ever before – but processing power is also economical. It cannot keep up with how fast we produce data or how cheap it is to persist it indefinitely, but processing is powerful enough to handle these volumes using certain technologies while still being affordable.” – Douglas McDowell CEO, North America
The traditional structured relational data warehouse was designed to be a central repository for all data in a company. Disparate data from transactional systems, ERP, CRM, and applications could be cleansed – that it extracted, transformed, and loaded (ETL) – into a data warehouse. As organizations add ever-expanding data sources, need real-time information from those sources, and expect sub-second performance from their data stores, the modern data warehouse with its variety of structured and unstructured data types becomes the new wave of Big Data solutions.
Data Warehouse Generation (or ETL)
Extraction, transformation and loading (ETL) is the component that carries the highest risk but also the highest value of any data warehouse initiative. Applying a proven ETL framework that facilitates standardized development and minimizes deployment and operation headaches ensures this value.
SolidQ delivers a modern data warehousing solution that delivers comprehensive logical data and analyics, using a complete suite of fully supported solutions and technologies.
Data Management and Processing
After assessing your environment and partnering with Business and IT stakeholders, SolidQ’s modern data warehousing solutions starts with the ability to implement relational and non-relational data sources like Hadoop. We help our clients handle data in real time, augment internal data with external data, and provide an analytics engine for predictive analytics.
Data enrichment and ETL
SolidQ provides our clients with the highest level of technical expertise when enriching your data with Extract, Transform and Load (ETL) capabilities. We also support credible and consistent data through data quality and master data management services.
Parallel Data Warehouse
Microsoft’s SQL Server 2012 Parallel Data Warehouse (PDW) enables you to scale up your relational data warehouses with a massively parallel processing appliance that scales to hundreds of terabytes of data. SolidQ’s Parallel Data Warehouse offering helps you plan a PDW installation, migrate data, determine the right architecture and deploy supporting data marts around your PDW installation.
Architecture and Design
SolidQ can design, architect and execute a data warehouse that meets the specific needs of your dynamic business. We can also conduct architecture and design reviews, ensuring your infrastructure is sustainable for tomorrow’s challenges.
Making better business decisions involves asking better questions. Data mining also involves asking better questions. Data mining empowers decision makers to answer the question no one thought to ask.
Modern (Logical) DW
Data warehouse systems are built with a long and often expensive development process. At times, these systems don’t meet your company’s needs. Extensive analysis is needed prior to building out the data warehouse infrastructure.