In-Memory Database Systems and Solutions

Big data and analytics have become a major competitive differentiator, but managing massive amounts of data with around-the-clock uptime is an ongoing challenge for IT. It’s more critical than ever to achieve the performance, availability and robust security that enterprises need for mission-critical workloads while keeping costs low.

“What’s an In-Memory Database system?”

Mainly, an in-memory database system (IMDB) is a database management system that relies on main memory (RAM) for computer data storage to facilitate faster response times. The following are some key points In-Memory Database systems have:

In-Memory Database systems use RAM as a first class storage layer in order to read and write directly to and from memory without touching the disk.
Source data is loaded into system memory in a compressed format, that means that the footprint needed on traditional database systems is reduced considerably.
In-Memory Database systems is one type of analytic database, which is read-only system, stores historical/non-frequently used data on disk.
IMDB systems allow users to run queries and reports on the information contained, which is regularly updated to incorporate recent transaction data from organization’s operational systems.
In addition to providing extremely fast query response times, in-memory analytics can reduce or eliminate the need for data indexing and storing pre-aggregated data in OLAP cubes or aggregate tables. This capacity reduces IT costs and allows faster implementation of BI/BA applications.
In-Memory Database systems provide internal optimized algorithms that are simpler and execute fewer CPU instructions.

Advantages of an In-Memory Database over traditional Databases

The purpose of the In-Memory Database is to provide increased performance over traditional database queries, especially for organizations that deal with big data or lack of performance regularly.

The premise behind the In-Memory Database comes from its ability to put the working set of either complete or partial data into system memory. In the case of partial data being moved in-memory, the tables selected are those that would benefit most from the increased speed gained from dynamic RAM storage, which brings us to the first advantage in-memory databases have over their traditional counterparts.

1. Speed (faster queries)

When a user queries a large data set, it takes time to process information requests when data is stored in traditional databases. Using In-Memory Databases help speed the queries because it takes much less time to search in-memory data. According to those who use in-memory databases, the speed difference is significant.

2. Real-Time Decision Making

All of the queries can be returned in the time it takes to get business done. In-memory data helps ensure that decision makers have the most relevant and timely information available when they are speaking to a customer, supplier, or even management in their own company. With this information, they’re able to present an accurate picture rather than having to wait for information to be returned or basing their decision on outdated information. This is why having these data is a definite competitive advantage.

3. Big Data Management

In-memory database helps in big data management because is used with applications that allow very fast data access, storage and manipulation even in systems that don’t have a disk but have the need to manage large volumes of data.

4. Real-time Updates

An important advantage when using an in-memory database system is its real-time embedded systems which are highly resource-constrained, require small memory and CPU footprint.

5. Reduced IT Costs

In-Memory analytics can reduce or eliminate the need for data indexing and storing pre-aggregated data in OLAP cubes or aggregate tables. This capacity reduces IT costs and allows faster implementation of BI/BA applications.

6. Reduced burden on IT Resources

How can these big databases, which commonly reside on large server hard drives, sit in memory?

PHOTO by William Warby under (CC BY 2.0)

This can be attributed to the low cost of memory. More memory is built into servers. Cost alone doesn’t account for how In-Memory databases work. Databases themselves have to be designed efficiently, which means less redundancy in data tables. Furthermore, data is compressed to help it fit within the smaller confines of memory as opposed to storage. As a result, there’s less need to purchase servers equipped with large hard disk drives, and resources are saved as a result of querying memory over power of consuming hard drives.

In-Memory Databases main players

1. MICROSOFT HEKATON

Microsoft SQL Server’s in-memory technology (Hekaton) is a new database engine optimized for memory resident data and OLTP workloads, fully integrated into SQL Server: it is not a separate system!! To take advantage of Hekaton, a user simply declares a table memory optimized.

Hekaton is designed around 4 architectural principles:

Optimize for main memory data access.
Accelerate business logic processing.
Provide frictionless scale-up.
Built-in to SQL Server.

Main Capabilities:

Achieve breakthrough performance with in-memory technology built in across all workloads.
Get the reliability and high availability you need with AlwaysOn.
Gain enterprise-class scalability and predictable performance.
Row Level Security and Always Encrypted technology in SQL Server 2016 protect data at rest and in motion.
SQL Server Management Studio helps to centrally manage database infrastructure across your datacenter and the cloud.

2. ORACLE TimesTen

Oracle TimesTen In-Memory Database is a full-features relational database that runs in the application tier, storing all data in main memory. This dramatically reduces latency and increases throughput.

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Oracle TimesTen In-Memory Database stores data in application tier main memory, and with no network latency or disk I/O, transactions take just microseconds and complex analytic queries happen at the speed of thought.
Provides enterprise-class reliability and availability by logging data and transactions to disk to enable a full recovery, and with high-speed replication, Oracle TimesTen In-Memory Database can be configured for high availability and instant failover.
Oracle TimesTen In-Memory Database is embedded in Oracle Exalytics In-Memory Machine, enabling Oracle Business Intelligence Standard Edition users to perform complex analytic queries at real-time speeds.
Oracle TimesTen In-Memory Database supports full SQL transaction semantics and includes OCI, Pro*C and PL/SQL for compatibility with Oracle Database.
Accelerates existing Oracle Database applications when used as a high-performance cache for Oracle Database, Enterprise Edition (see Oracle TimesTen Application-Tier Database Cache).

3. SAP HANA

SAP HANA in-memory database powers real-time insights across your business controlling systems and data.

Main Functional Capabilities

High-performance computing: It leverages the latest hardware and software innovations to accelerate performance for all applications.
Comprehensive data processing: Embeds multiple data processing engines and predictive libraries to maximize value from Big Data and the Internet of Things (IoT).
OLAP and OLTP support: Allows processing for transactional and analytic workloads on the same system with online transaction processing (OLTP) and online analytical processing (OLAP).
Administration and security: It helps you monitor system health and network security which are key tasks for administrators.
Integration services: All of your data sources can be integrated into SAP HANA – to complement your SAP HANA applications or to perform in-depth analyses.

Main Technical Capabilities

Open environment: Supports standard JDBC/ODBC and RESTFul Webservice to help you build applications that can be easily integrated with your legacy systems.
Componentized data integration: Allows maximum flexibility and shrink TCO with componentized data integration.
Efficient system Management: Integrates development, administration, and monitoring tools to manage systems more efficiently.

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