Starcounter, the combined in-memory database engine and application server provider, has secured $1.8 million in financing, they announced yesterday.

“We are impressed with the way Starcounter’s groundbreaking database technology helps developers bring new applications to market fast and at a low cost,” vouched Johan Englund, Investment Manager at Industrifonden. “Starcounter is positioned to make a significant impact on the software industry, and with this new financing, we look forward to the company’s continued growth and innovation in this market.”

$1.2 million of the total came from Sweden’s investment giant Industrifonden, which has a past of investing in companies it believes to possess international growth potential, according to a news release.

The new-found financing will aid in the development and launch of a new web technology for software application developers, all the while fueling the global outreach strategy and adoption of Starcounter’s in-memory application platform for independent software vendors (ISVs) and web developers.

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(Image credit: Starcounter)

Yiftach Shoolman is CTO & Co-Founder at Redis Labs, the largest commercial supporter of Redis with more than 70,000 databases under management and 3,700+ paying customers. Follow him on Twitter.

Prediction #1 – The Demand for In-Memory Databases Will Increase

The expectation from modern apps is to provide a response to any request in under 100ms. Assuming that the Internet’s average latency is 50ms, this leaves only 50ms for processing the request inside the datacenter, including front-end appliance overheads (such as firewalls, application security and load-balancers) and business logic processing by the web, application and database tiers. In many cases a single user request requires multiple calls to the database to prepare a response. This practically mandates sub-millisecond processing time at the database tier, which can’t be achieved without in-memory technologies.

Prediction #2 – In-Memory Databases Become More Than Just a Cache

With more than 70,000 databases under management at Redis Labs, we’ve noticed that a growing number of users migrate their data to Redis and use it as their primary database, rather than as a cache-only solution.  When your in-memory database provides rich functionality (e.g. various data structures, a robust command set and support for embedded scripts, like in the Redis world) and an enterprise-class feature set (see my next prediction), why would you split your already complex application logic across so many database technologies?

Prediction #3 – Enterprise-Class Features Become a Must Have

Everyone agrees that database scalability is important, but when it comes to in-memory databases, seamless and instant scalability is truly critical. Unlike disk-based database technologies, an in-memory database allows for massive and sudden dataset growth, often from a few gigabytes to terabytes in just minutes and bursts of hundreds of thousands operations per second in a few seconds.

The same is true for high-availability – when you run an in-memory database like Redis that can support hundreds of thousands of operations per second, you must have an instant auto-failover mechanism that takes a only a few seconds (single digit) to execute. The lack of such a mechanism will result in the loss of a significant amount of your writes, and will leave your application in an inconsistent state. Furthermore, while it is understood that an in-memory database should be equipped with data persistence mechanisms for durability, achieving that goal without significantly degrading database performance is a major challenge. While it is expected that a number of in-memory databases will boast support for a fast data-persistence engine in 2015, their performance may be severely limited unless implemented correctly.

Lastly,  in-memory databases should be deployed across data-centers and geographical regions in order to increase the your app’s availability and retain the performance of an in-memory database even with a multi-site deployment.

Prediction #4 – In-Memory Will Be More than Just DRAM

With state-of-the-art flash arrays, high throughput SSDs and the new Storage Class Memory products created out of flash-based NAND, achieving multi-million IOPS at sub 10 microsecond latency is no longer fiction. A well designed in-memory database can utilize these new technologies to run at near DRAM performance without the high cost of deployment associated with RAM-only databases.

Predication #5 – Multiple Delivery Models Will Become More Pervasive

As mentioned earlier, it is extremely important for an in-memory database to be deployed as close as possible to your application servers to avoid network latencies and bandwidth costs. At the same time, the freedom to choose between different deployment models is equally important. A fully managed database-as-a-service allows you to ‘deploy and forget’ your in-memory database and operate everything with minimal ops. On the other hand, it prohibits you from deploying the database at any arbitrary location. That is the reason why several in-memory database vendors are starting to provide a hybrid delivery model in which you can use a database-as-a-service for the parts of your application that run on the cloud, as well as downloadable database software for on-premise, private cloud environments. Some of these solutions come with a tool that even allows you to synchronize between the two deployment models in a secure manner.

Predication #6 – Open Source Technology Will Win

The majority of new emerging in-memory databases are based on open source projects. But how many of these are truly open source projects rather than the development efforts of the sponsoring company’s employees?

It is nearly impossible for a database vendor that open sources its code, seemingly as an afterthought, to compete with a real open-source project that has been developed for years by a vibrant community. The value of a strong open source project with an abundance of clients, libraries, use cases and deployment options is unparalleled. Furthermore, when your in-memory database is based on a real open source project, developers with relevant knowledge and experience are easier to find.

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Companies offering cloud-based analytics platforms have received considerable attention over the past few years and it is undeniably a hot place to be today. Take the release of Oracle RightNow Analytics Cloud last week, as an example of this, or GoodData’s recent funding announcement. SalesForce is also preparing an analytics cloud plan, which is rumoured to be announced next week. Given the extreme shift towards cloud analytics, companies in this space have to offer something unique to differentiate themselves from their competitors.

In an attempt to do just this, SAP and Birst today announced that they will extend their partnership to combine SAP HANA’s Cloud Platform with Birst’s analytics capabilities. Branding it as instant analytics offering, the partnership is aimed at addressing a key enterprise problem in the market right now– that is, the speed at which companies can ingest data from different sources.

Birst’s analytics engine builds a user-ready data store in SAP HANA that can deliver visual discovery, dashboards and enterprise reports across a single business model, accelerated by SAP HANA. Crucially, however, the solution will be designed to provide instant analytics across on premise and cloud data sources like salesforce.com, Oracle, SAP and Hadoop.

Speaking to Dataconomy about the announcement, SAP said, “Leveraging the power of the HANA platform, we think Birst can have a much more scalable and performance solution. Ultimately, the partnership will allow Birst to drive real-time capability, provide better integration with SAP and non-SAP solutions, and ultimately provide more customer choice.”

To understand how the partnership will really differentiate Birst’s cloud analytics offering from other companies like Oracle and Salesforce, Brad Peter’s told us that they have been developing their product so that people do not have to make significant compromises like they would with other providers like Oracle or Salesforce.

“Doing analytics in the cloud is not easy, you cannot decide overnight that you want to do it. Everyone who does that has typically made some very significant compromises that will inevitably limit your ability to do real enterprise calibre analytics.”

“In the case of Oracle, it’s simply a hosted database that looks more like an ASP than a cloud offering and this is something we need to keep in mind, whether it’s really cloud or not,” Brad continued.

“When it comes to Salesforce’s offering next week, I’m sure it will be flashy insofar as the simple problems it deals with around Salesforce-only data. But when you deal with richer Enterprise data that needs to be brought together form different sources that’s a much more challenging problem that I think the Birst and HANA is uniquely suited for.”

Image Credit: SAP

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Gridgain announced yesterday that their in-memory data fabric has been accepted into the Apache Software Foundation Incubator programme, under the name Apache Ignite. The Gridgain team hope the move will fuel greater adoption of in-memory computing technologies, and build a greater community around the data fabric.

Expanding upon their motives to open-source their signature product, a Gridgain blogpost states:

Earlier this year, we took an important step in order to make the transformation to IMC truly accessible by shifting to an open source model through an Apache 2.0 license. Our motivation was simple: remove all barriers to IMC adoption and accelerate the innovation that’s poised to occur through this technology. We wanted to introduce IMC to the mass market in a way that would give developers maximum freedom to experiment with it and discover the amazing things it allows you to do — for example, accelerating drug discovery for diseases such as cancer.

As Dataconomy reported earlier this year, Gridgain’s In-Memory Data Fabric accomodates high-performance transaction, real-time streaming and lightning-fast analytics to help enterprises get the answers they need when they need them. The Data Fabric is the product of “years of meticulous research and development”, and now this powerful technology is available to enterprises of all sizes to gain business-critical insights in real-time.

Only a few short months after open-sourcing their technology, Gridgain reported a 2000% increase in downloads. It is hoped that by handing the keys over to the Apache Software Foundation, downloads will spike once more, move Gridgain closer to their goal of becoming synonymous with in-memory computing. As their recent blog post ambitiously concludes, “We believe Apache Ignite has all the right ingredients to become for the Fast Data world of the future what Hadoop is for Big Data today.”

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(Image credit: Gridgain)

Pivotal, the San Francisco-based software and services provider has announced its partnership with the AMPLab at UC Berkeley to support the in-memory Tachyon project. They aim to bolster the data lake technology with an architecture that builds upon disk-based storage with memory-centric processing frameworks.

Being led by Haoyuan Li, a PhD candidate at the UC Berkeley AMPLab, Tachyon is a memory-centric, fault-tolerant distributed file system that allows for reliable data exchange at in-memory speed across cluster frameworks, explains their Blog. Tachyon will be a central data exchange layer for the Pivotal Big Data Suite.

Till date this project is the fastest-growing project in all of AMPLab history, ahead of Mesos and Spark. It is currently a GitHub project developed under the Apache License.

EMC’s Chief Technology Officer John Roese said, ”EMC is excited to join with Pivotal in supporting and fostering the rapidly innovating Big Data ecosystem. The next wave of real-time analytics will be made possible by technologies such as Apache Spark and Tachyon, in combination with innovative storage products such as EMC’s recent DSSD acquisition.”

Pivotal which is a spin-out and joint venture of EMC Corporation and VMware has pledged resources to Tachyon’s development and stands as the number one corporate contributor to the Tachyon code base.

Read more here.

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(Image credit: Tachyon)

It’s no secret that in-memory, real-time applications have been gaining ground for quite some time. The idea that data should nurture immediate decision-making and not just be confined to historical, retrospective analysis has certainly proved to be a powerful one. Now, open source in-memory powerhouse Gridgain have unveiled their latest innovation for real-time insights; the Gridgain In-Memory Data Fabric.

The technology accomodates high-performance transaction, real-time streaming and lightning-fast analytics to help enterprises get the answers they need when they need them. According to Gridgain, the Data Fabric is the product of “years of meticulous research and development”, and it’s hoped that this technology will prove to be a game-changer for Gridgain and the in-memory computing market.

Abe Kleinfeld, CEO of Gridgain told Dataconomy: “The GridGain In-Memory Data Fabric provides an easy on-ramp to in-memory performance and scale for both legacy applications and new development, while also breaking the hard-wiring of vendor lock-in in the former and enabling unprecedented opportunity for innovation in the latter.”

Gridgain have aimed to increase the market of the In-Memory Data Fabric by offering API-parity with a range of applications (including Java, .NET, C++, Hive & MapReduce), as well as connections with a range of data stores (including SQL, NoSQL and Hadoop). Pair that with ramped-up security options and high availability, and Gridgain certainly have a tempting product for enterprises looking to gain insights in a more timely fashion.

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The German software giant SAP announced yesterday that it has expanded its integration with Esri, a mapping specialist that provides geospatial services and content. The partnership brings high-performance spatial analytics, self-service mapping and collaboration to geographic information system (GIS) and business users to allow them to leverage real-time location intelligence in both their Esri and SAP environments.

The announcement will see Esri’s mapping technology integrated across the SAP Hana in-memory database, core SAP enterprise applications, the BusinessObjects analytics portfolio, and the SAP Mobile platform. Taking advantage of Hana’s rapid processing times, customers can now avoid moving data out of Hana and into Esri’s ArcGIS server – instead ArcGIS querying can take place natively inside SAP Hana, where the data is stored.

Moreover, as one article reports, the extended integration will also support ArcGIS self-service mapping and collaboration features with SAP Hana as a high-performance engine of the business data that is geospatially enabled.

Steve Lucas, president of platform solutions for SAP, said in a statement,

“By integrating ESRI’s industry leading GIS with SAP HANA, the SAP BusinessObjects BI platform and SAP Mobile Platform as well as geospatial analytics within SAP Lumira, we are enriching business data with geographical context and presenting it in real time — bringing a whole new level of insight to customers.”

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Oracle announced yesterday that it has launched its in-memory option for its Database 12c, which will be available within 30 days. Although the pricing has not been disclosed, the company said that it would release the official details once the Oracle Database In-Memory is generally available.

“We’re constantly evolving,” said Oracle’s co-founder and chief executive, Lawrence Ellison. “The technology around our database changed. Memory became cheaper, so it made sense to use more. Flash memory became a way to replace disk memory. Networks became faster.”

The announcement of Oracle’s in-memory capabilities will allow admins to store information in the memory of a server as opposed to being stored on spinning disks that can often be located far away from chips. Given that the performance of memory is considerably faster than disk, Oracle customers can retrieve and process huge amounts of data and “respond quickly to changing business conditions.”

Indeed, Oracle is not the first mover in this field. Rival company SAP has been selling its own in-memory technology (HANA) for number of years now and has grown to prominence, especially since HANA has become easier for companies to consume in cloud-based versions.

However, according to an article in the New York Times, Oracle may be an improvement over HANA. “Mr. Ellison demonstrated a machine that could analyze nearly one trillion rows of data per second, and hold 32 trillion bytes of data in memory. A process that had previously taken 58 hours, he said, now needed only 13 minutes.” Although, it must be noted, a process like this could cost a significant amount of money, and since Oracle has not released pricing options, it is unclear how it will compete with HANA.

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(Image Credit: Peter Kaminski)

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