In-Memory Analytics Market SWOT Analysis, Industry Revenue, Growth Overview And Forecast To 2033

The new report published by The Business Research Company, titled ""In-Memory Analytics Global Market Report 2024 - Market Size, Trends, And Global Forecast 2024-2033"", delivers an in-depth analysis of the leading size and forecasts, investment opportunities, winning strategies, market drivers and trends, competitive landscape, and evolving market trends.

As per the report, the in-memory analytics market size has grown exponentially in recent years. It will grow from $4.52 billion in 2023 to $5.66 billion in 2024 at a compound annual growth rate (CAGR) of 25.1%. The in-memory analytics market size is expected to see exponential growth in the next few years. It will grow to $14.43 billion in 2028 at a compound annual growth rate (CAGR) of 26.4%.

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Rising Data Volumes Propel In-Memory Analytics Market Growth As Demands For Efficient Data Processing Soar
The growing volume of data is expected to drive the growth of the in-memory analytics market. Growing data volume is increasing demand for large space in the system's memory which leads to obstruction and delay in the business process. In-memory analytics is used to minimise the data into samples or reduce the data to a small volume by not changing the actual meaning and increasing the query response time. For instance, the International Data Corporation (IDC), a US-based examiner of the consumer market, estimated that 64.2ZB of data was created or copied in 2020. The amount of data created throughout the next five years will be larger than twice the volume of data created since the advent of digital storage. This is likely to drive the in-memory analytics market growth during the forecast period.

Gigaspaces Launches Insightedge For Enhanced Performance And Scale
Technological advancement is a growing trend that is gaining key popularity in the in-memory analytics market. The companies operating in the in-memory analytics market are developing advanced technologies that will help in reducing the time for operation and complexity of the data by providing an enhanced product to their clients. For instance, In December 2021, GigaSpaces, a US-based software company, announced the launch of the InsightEdge portfolio, a new set of in-memory computing platforms that are designed to drive the enterprise digital transformation with unique speed, performance, and scale. Combined with the AIOps functionality, GigaSpaces will deliver effortless management and deployment of a portfolio of software platforms that meet the most stimulating analytics processing and enterprise data needs. AIOps refers to artificial intelligence for IT operations that provides analytics technology and enhances IT operations.

The in-memory analytics market covered in this report is segmented -

1) By Component: Solution, Service
2) By Organization Size: Large Enterprises, Small and medium Enterprises (SME's)
3) By Deployment Mode: On-premise, Cloud-Based

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Major companies operating in the in-memory analytics market report are Amazon Web Services Inc., Oracle Corporation, Qlik Technologies Inc., SAP SE, SAS Institute Inc., Software AG, International Business Machines Corporation, ActiveViam Ltd., Kognitio Holdings Ltd., MicroStrategy Incorporated, ADVIZOR Solutions Inc., EXASOL AG, SnappyData Inc., Teradata Corporation, GigaSpaces Technologies Inc., Information Builders Inc., Hitachi Ltd., Altibase Corp., GridGain Systems Inc., Pivotal Software Inc., TIBCO Software Inc., Altair Engineering Inc., Hazelcast Inc., MemSQL Inc., ScaleOut Software Inc., Aerospike Inc., Couchbase Inc., Redis Labs Inc., VoltDB Inc., Grid Dynamics International Inc.

Contents of the report:
1. Executive Summary
2. In-Memory Analytics Market Report Structure
3. In-Memory Analytics Market Trends And Strategies
4. In-Memory Analytics Market - Macro Economic Scenario
5. In-Memory Analytics Market Size And Growth
…..
27. In-Memory Analytics Market Competitor Landscape And Company Profiles
28. Key Mergers And Acquisitions
29. Future Outlook and Potential Analysis
30. Appendix

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