#in memory analytics

[theshubham01]

The global In-Memory analytics market revenue was valued at $1.956 billion in 2018, and is projected to reach $11.858 billion by 2026, growing at a CAGR of 25.40% from 2019 to 2026. Asia-Pacific is expected to witness highest growth rate during the forecast period, owing to adoption of in-memory analytics by various SMEs for managing huge quantity of data.

The report analyses the profiles of key players operating in the market Amazon Web Services, Inc., SAP SE, Oracle Corporation, SAS Institute Inc., International Business Corporation, Hitachi, Ltd., Software AG, MicroStrategy Incorporated, Qlik Technologies, and Kognito Ltd. These players have adopted various strategies to increase their market penetration and strengthen their position in the industry.

By region, the global in-memory analytics market was dominated by North America in 2018 and is expected to maintain this trend during the forecast period. The major factors that drive the growth of the market in this region include increase in demand for in-memory database system for quick storage and fast transfer of files, presence of major market players, and technical expertise in this region.

[isuperbinternetcollectorus]

At a CAGR of 25.1% In-Memory Analytics Market size is projected to exceed $3.85 Billion by 2022

According to new market research report on "In-Memory Analytics Market by Component (Software, Services), Application (Risk Management & Fraud Detection, Sales & Marketing Optimization, and Financial Management), Deployment, Organization Size, Vertical, Region - Global Forecast to 2022", The in-memory analytics market size is expected to grow from USD 1.26 Billion in 2017 to USD 3.85 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 25.1%.

The key forces driving the in-memory analytics market include the digital transformation using real-time analytics, technological advancement in computing power, growing data volume, and demand for self-service Business Intelligence (BI) tools.

Browse and in-depth TOC on “In-Memory Analytics Market”

72 - Tables

40 - Figures

154 - Pages

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North America is expected to constitute the largest market share; Asia-Pacific (APAC) to grow at the highest CAGR

North America is expected to continue its dominance in terms of market share throughout the forecast period. Early adoption of new & emerging technologies and large number of in-memory analytics vendors in the region would help consolidate the majority of revenues in the market. The APAC region is expected to grow at the highest CAGR during the forecast period. Owing to the growing technology expenditures in countries, such as China & India and the demand for cost-effective analytical software & services among Small and Medium-Sized Businesses (SMBs) would drive the market growth during forecast period.

Risk management and fraud detection application segment is expected to constitute the highest market share during the forecast period

The organizations implement in-memory analytics solutions to enhance risk intelligence capabilities to fight with operational and financial risks. The companies are using advanced analytical tools to avoid, address, and quickly recover from major risk events. The risk management and fraud detection solution helps identify, monitor, analyze, and control risks.

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In-memory analytics software component is expected to contribute the largest market share in 2017

Among components, the in-memory analytics software is expected to continue its dominance throughout the forecast period. In-memory analytics solution empowers the user to perform quick data analysis and achieve real-time insights from the stored data. The falling Random Access Memory (RAM) prices and technological advancements in computing power have propelled the adoption of in-memory analytics software among end-users.

The major vendors providing in-memory analytics software and services are SAP SE (Walldorf, Germany), MicroStrategy Incorporated (Virginia, U.S.), Kognitio (Berkshire, England), SAS Institute, Inc. (North Carolina, U.S.), Hitachi Group Company (Tokyo, Japan), ActiveViam (London, UK), Oracle Corporation (California, U.S.), IBM Corporation (New York, U.S.), Information Builders (New York, U.S.), Software AG (Darmstadt, Germany), Amazon Web Services (Washington, U.S.), Qlik Technologies, Inc. (Pennsylvania, U.S.), ADVIZOR Solutions (Illinois, U.S.), and EXASOL (Nuremberg, Germany).

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[ptssystem]

Meaning and Arrangement Offerings for In Memory Analytics

In Memory Analytics is a fairly dull name, however, it speaks to a significant change in perspective in how associations use the information to handle an assortment of business challenges. With In Memory Analytics, every one of the information utilized by an application is put away within the main memory of the computing condition. As opposed to accessing the information on a circle, information remains suspended in the memory of a ground-breaking set of PCs. Numerous clients can share this information over different applications quickly, safely and simultaneously. In Memory Analytics is a way to deal with querying information when it lives in a PC's arbitrary access memory (RAM), instead of querying information that is put away on physical circles. This outcomes in incomprehensibly abbreviated inquiry reaction times, allowing business intelligence (BI) and scientific applications to help quicker business decisions.

[ptssystem]

In Memory Analytics

In Memory Analytics can decrease or eliminate the requirement for information indexing and storing pre-accumulated information in OLAP shapes or total tables. This lessens IT costs and permits quicker execution of BI and systematic applications. It is foreseen that as BI and diagnostic applications grasp In Memory Analytics, customary information distribution centers may in the long run be utilized distinctly for information that isn't questioned as often as possible. 

Difficulties for Building In Memory Analytics Platform 

Need In-Memory answers for two cases – 

To improve the presentation of Healthcare application with .NET and SQL Server Architecture. 

Perform quick investigative inquiries on Apache Spark and Hadoop based network Data Warehouse. 

Arrangement Offerings for In Memory Analytics 

First Use Case 

Use Apache Ignite as an In-Memory database to improve the presentation of SQL inquiries with Ignite In-Memory information texture for .NET. For ACD exchanges, SQL Queries and appropriated SQL joins. 

Second Use Case 

Use Apache Ignite as IGFS and shared memory layer Spark RDD using Ignite RDD and construct Analytics Dashboard using Play structure to interact with Apache Ignite using its API. The dashboard arrangements client to transfer semi-organized information in different organizations, for example, CSV, JSON, and so forth and run diagnostic inquiries.

 PTS Systems & Solutions Pvt. Ltd

Office # 701, Haware Infotech Park, Sector 30a, Vashi, Mumbai - 400703

 22-20870275 / 9029968387

 [email protected]

[ptssystem]

In Memory Analytics

In Memory Analytics can decrease or eliminate the requirement for information indexing and storing pre-accumulated information in OLAP shapes or total tables. This lessens IT costs and permits quicker execution of BI and systematic applications. It is foreseen that as BI and diagnostic applications grasp In Memory Analytics, customary information distribution centers may in the long run be utilized distinctly for information that isn't questioned as often as possible. 

[reportsandmarkets]

In Memory Analytics Industry, 2012-2022 Market Research Report

In Memory Analytics Industry, 2012-2022 Market Research Report’ is a professional and in-depth study on the current state of the global In Memory Analytics industry with a focus on the Chinese market.

Download sample pages of given report @ https://www.algororeports.com/sample-request/global-and-chinese-in-memory-analytics-industry-2017-market-research-report

The report provides key statistics on…

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[inmemorydatabase-blog]

In Memory Database | In Memory Analytics Engine | In Memory Engine

In-Memory Database is a memory-optimized relational database that provides applications with the instant responsiveness and very high through put required by today's real-time enterprises in a wide range of industries. A Database resides entirely in memory at run time and is persisted to disk storage for the ability to recover and restart. Applications access the database using the JDBC, ODBC, OCI, ODP.NET and/or Pro*C/C++ interfaces. It is fully transactional, persistent, and highly available with transactional replication. And is typically deployed in the middle-tier with the applications; it can be run as a stand-alone database or as an in-memory cache database for an Oracle database. Using in-memory cache database provides applications the power of SQL, the speed of RAM and the reliability of a proven product with automatic data synchronization between the in-memory cache tables and the backend Oracle database.

At the Oracle OpenWorld annual user conference last week, Oracle announced enhancements to the Oracle Database 12c platform, including a new in-memory option for both analytics and transactional applications and an extension of its engineered systems portfolio to cover backup and logging. The new Oracle Database In-Memory Option is targeted at improving performance with analytics and OLTP (online transaction processing).

The in-memory enhancement is in line with overall trends that Ovum has identified with data platforms adding the capability to handle more diverse workloads. Oracle also announced a new appliance with a long – but very descriptive – name: the “Oracle Database Backup Logging Recovery Appliance.” It is designed to make backup and recovery more manageable, reliable, and up to date.

The latest addition to Oracle’s engineered systems line does not add new capabilities beyond those available from software-only solutions from providers like Informatica. But it fills an important gap for Oracle’s engineered systems platforms to make them a more complete, self-reliant platform environment.

In-memory a natural step in data platform evolution:

As enterprises face snowballing demands, not only to process larger or more diverse data sets, but also to more readily blend analytics into operations, transactional data platforms are adding new analytic capabilities that are increasingly set apart from the OLTP system. Oracle has joined IBM in appending capabilities to its core database platform that increase the performance and capability of analytics with separate analytic engines.

Killing two birds with one stone:

The obvious benefit is that it accelerates analytic processing compared to conventional disk storage. Oracle claims OLTP processing that is twice as fast, and analytic processing that is up to 100 xs faster over the initial release of the 12c database generation.

The key to these benefits is that incorporating in-memory storage allows database designers to reduce or even eliminate analytic indexes. That, in turn, can greatly reduce database footprint, because indexes can multiply storage requirements geometrically (depending on the range of queries for which the database is modeled). And, while analytic indexes are used to speed query performance, burdening a database with multiple indexes typically slows OLTP performance.

In-memory is the latest refinement on the established practice of data tie ring, where the most frequently used data is placed on the fastest storage. Traditionally that was on disk, but with the pricing of memory having dropped (until recently), it has become affordable to add memory as the fastest data storage tier.

Oracle is not the first to exploit in-memory; for instance, IBM, Tera data, and SAP HANA support it to varying degrees. Oracle is unique in that it is pairing it with a disk- and in-memory-based row store that will instantly replicate data to columnar tables. By comparison, SAP HANA (which offers a full in-memory store) requires the user to make an either/or decision of storing the data in row or in columnar format. By comparison, Teradata is only an analytic platform and IBM does not automatically treat in-memory columnar data as replicated from the transaction row store side.

It reduces overhead and adds currency by conducting backups continually, with automatic checks to ensure backups are valid. Compared to software-based solutions, the appliance promises speed: backups are completed within sub seconds. But there is one major difference between Oracle’s appliance-based backup and generic software solutions: the Oracle appliance only supports the Oracle database; that makes it a tight fit for all-Oracle installations, but not for mixed database environments.

The new appliance fills an important gap in Oracle’s strategy for high manageability in its engineered systems by extending the umbrella to the often-overlooked processes of backup and restore.

About Luxon:

At Luxon, we believe that data is emerging as the world's newest resource for competitive advantage, and analytics is the key to make sense of it. To make Enterprise Analytics work effective, organizations need best in class hardware to run its business applications. Luxon is a Universal In-Memory Analytics engine, designed and developed by technology experts to serve the needs of global organizations in small and medium sector (or) SME’s. Luxon aims to cut down the costs for running your organizations analytics by 50 percent compared to existing analytics engines like HANA, Exalytics. Luxon supports all the traditional databases including latest innovations like SAP HANA, Oracle 12C. Luxon runs your reports 100X faster compared to your traditional engines. Luxon is built on reliable and proven best in class Hardware system architecture.  Follow Luxon Analytics on Twitter @LuxonIn.

For more information about Luxon In-Memory Engine visit www.LuxonInMemory.com