Slideshow Big Data: 14 Requirements for Real-Time Analytics

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  • February 22 2015, 1:20pm EST
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Big Data: 14 Requirements for Real-Time Analytics

To build a successful real-time business analytics system, you'll likely need an in-memory database plus several other core technologies. Here's a look at each potential piece of a real-time analytics solution. Special thanks to Wikibon for background information.Image: iStock

1. Adopt In-Memory Databases

Through the use of in-memory databases, tables and key information are held in main memory (vs. traditional on-disk database systems). Working in main memory is much faster than writing to and reading from a file system, notes McObject. As a result, in-memory databases can perform an application’s data management function an order of magnitude faster than traditional databases, McObject asserts.Image: iStock

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2. Embrace DRAM Memory

The DRAM memory is protected against power failure by battery or capacitance technologies. It typically uses high-speed flash to protect and recover or restore data, notes Wikibon.Image: iStock

3. Leverage Flash

All other data is held on high-performance flash technology. The reason: Any traditional disk or hybrid disk technology typically bottlenecks performance. Historically, flash innovations demanded a price premium. But consumer demand for flash continues to drive down costs across the board. Plus, scale-out flash array architectures allow physical data to be shared across many applications without impacting performance.Image: iStock

4. Go Parallel

All processing should be highly parallelized – with high-bandwidth, low-latency interconnects between processors, memory and flash technologies (where necessary).Image: iStock

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5. Think Bigger

All of your metadata about the data should be held in DRAM to maximize performance.Image: iStock

6. Think Ahead

Make sure the system supports anticipatory fetching and processing, which enables faster access to supporting data from multiple data streams.Image: iStock

7. That Makes Sense

Logical sharing of single copies of data should be built into the system.Image: iStock

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8. Know Your Options

Choosing an in-memory database requires plenty of research. Five potential options include Aerospike, IBM Blu, Microsoft, Oracle and SAP Hana.Image: iStock

9. A Closer Look: Aerospike

Aerospike is a flash-optimized, in-memory open source NoSQL key-value database. It handles very high volume streams of data. Enterprises with existing transaction applications would need to migrate them to Aerospike for real-time analytics integration.Image: Aerospike

10. A Closer Look: IBM BLU

IBM BLU is based on the standard IBM DB2 OLTP 10.5 offering. BLU Acceleration capabilities are designed mainly for “read-mostly” inline analytics. It can leverage SIMD (single instruction, multiple data) on IBM Power 7 or Power 8 chips to improve performance. APIs in DB2 can potentially ease migration from Oracle.Image: iStock

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11. A Closer Look: Microsoft

Microsoft SQL Server 2014 now has an In-Memory OLTP extension. Integration with SQL Server means you can have both memory-optimized tables and disk-based tables in the same database, and query across both types of tables, Microsoft asserts.Image: iStock

12. A Closer Look: Oracle

Oracle’s database now offers an in-memory option (additional costs involved) for performing analytic queries in parallel. It integrates with high-availability options such as ORACLE RAC and Dataguard.Image: iStock

13. A Closer Look: SAP HANA

HANA stands for High-Performance Analytic Appliance. Generally speaking, SAP HANA’s greatest value is in providing specific operational reports within minutes – rather than days.Image: iStock

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14. Bonus Content

Check out all of Information Management’s slideshows here.Special thanks to Wikibon for the guidance and tips shared in our real-time analytics slideshow. Information Management’s perspectives are also interjected throughout. You can find additional in-memory database options here.Image: iStock