IDC's Infrastructure: Cognitive/Artificial Intelligence, Big Data and Analytics Workloads service looks at the impact of new machine learning and analytics technologies on infrastructure software and hardware markets. Specific focus is put on the infrastructure needs for newer technologies such as nonrelational analytic data stores (e.g. Hadoop, MongoDB, Cassandra) and continuous analytic tools (e.g. Amazon Kinesis, Splunk Universal Forwarder, Microsoft Azure Data Factory). The program also covers infrastructure needs for relational data warehouses, analytic and performance management applications, and business intelligence and analytic tools and platforms (including cognitive/AI software platforms). The impact to infrastructure is examined across compute and processor architectures, storage interfaces and system types, data organization, storage capacity, and revenue for primary, secondary, and archive tiers. These data points will be used for segmentation and forecasting.
Markets and Subjects Analyzed
- Computing requirements across general purpose and accelerating computing and storage infrastructure used in support of new workloads
- Infrastructure types (discrete or converged/integrated), array types (all-flash, hybrid storage or HDD), data organizations and in-memory technologies supported
- Deployment location (on/off premises) and consumption model (traditional/as-a-service) preferences
- Implications of Big Data and analytics on data lifecycle management use cases such as production, backup, replication, and archive
- Computing and Storage Infrastructure for Cognitive/Artificial Intelligence, Big Data and Analytics Taxonomy
- Infrastructure for Cognitive/Artificial Intelligence, Big Data and Analytics Segmentation, Market Size and Forecast Report
- Infrastructure for Cognitive/Artificial Intelligence, Big Data and Analytics End User Adoption Studies
- Qualitative Assessment of Infrastructure Innovations Impacting Cognitive/Artificial Intelligence Workloads
- Qualitative Assessment of Big Data and Analytics Use Cases and Applications
In addition to the insight provided in this service, IDC may conduct research on specific topics or emerging market segments via research offerings that require additional IDC funding and client investment.
Key Questions Answered
- What is the infrastructure spending in support of cognitive/Artificial Intelligence and Big Data and analytics workloads?
- What percentage of enterprise infrastructure will employ cognitive/machine learning algorithms and for what use cases (predictive analytics, preventive maintenance, intelligent data placement)?
- What percentage of cognitive/Artificial Intelligence, Big Data and analytics technologies will be hosted on-premises, public cloud and hybrid/multicloud stacks?
- What percentage of cognitive/Artificial Intelligence, Big Data and analytics result sets will be retained? For how long, and what approaches will be used?
- Will compute and storage in support of cognitive/Artificial Intelligence, Big Data and analytics workloads be scale-out and industry-standard hardware based or leverage a shared storage file architecture?
- What will be the role of technologies like accelerated computing, NVMe, tiering, deduplication, and compression as they are related to cognitive/Artificial Intelligence, Big Data and analytics?
DataDirect Networks, Inc.,
Hewlett Packard Enterprise,
MapR Technologies, Inc.,
NEXENTA SYSTEMS INC,
NIMBUS DATA SYSTEMS, INC.,
Nimble Storage, Inc.,
Pure Storage, Inc.,
Red Hat, Inc.,
SAS Institute Inc.,