Category Archives: Cassandra

Amazon, Avinash Lakshman, Cassandra, Facebook, Funding, Hedvig, News & Analysis, software defined storage

Hedvig bags $18m for software-defined storage

Hedvig secured $18m this week which will help fuel expansion of its software-defined storage offering

Hedvig secured $18m this week which will help fuel expansion of its software-defined storage offering

Distributed storage platform provider Hedvig has secured $18m in a round of funding the company said will be used to double down on development and expansion.

In the cloud space storage heterogeneity can cause big performance bottlenecks – particularly in tightly integrated systems, which many applications and services are quite clearly becoming – and legacy datacentres are struggling to keep pace.

Hedvig, which came out of stealth earlier this year and was founded by former Facebook and Amazon NoSQL and storage specialist Avinash Lakshman (also the brains behind Cassandra), offers a highly scalable storage platform (block, file and object) that the company says provides fully programmable, highly granular storage provisioning – software-defined storage in other words.

The platform supports pretty much every hypervisor or Linux container service above it, and uses REST-based APIs so cloud users can tap into the platform in a fairly straightforward way.

The investment round, which brings the total amount raised by the firm to just over $30m, was led by Vertex Ventures with participation from existing investors True Ventures and Atlantic Bridge. As part of the deal Vertex Ventures General Partner In Sik Rhee will be joining Hedvig’s board of directors.

“We’ve identified the potential in a broken and fragmented storage market, and are not only looking to bring software-defined storage mainstream, but fundamentally change how companies store and manage data,” Lakshman said.

“Riding the wave of momentum from our recent company launch, this new investment round further validates our technology and approach, and will fuel our unwavering commitment to be the leading force of innovation in software-defined storage.”

Hedvig’s success comes at a time of rising popularity of the concept of the software-defined datacentre, which sees the orchestration of almost everything – storage, compute, networking – through software.

Apache Hadoop, Big Data, Cassandra, Guest Post, Real-time computing

Real-Time Processing Solutions for Big Data Application Stacks – Integration of GigaSpaces XAP, Cassandra DB

Guest post by Yaron Parasol, Director of Product Management, GigaSpaces

GigaSpaces Technologies has developed infrastructure solutions for more than a decade and in recent years has been enabling Big Data solutions as well. The company’s latest platform release – XAP 9.5 – helps organizations that need to process Big Data fast. XAP harnesses the power of in-memory computing to enable enterprise applications to function better, whether in terms of speed, reliability, scalability or other business-critical requirements. With the new version of XAP, increased focus has been placed on real-time processing of big data streams, through improved data grid performance, better manageability and end-user visibility, and integration with other parts of your Big Data stack – in this version, integration with Cassandra.

XAP-Cassandra Integration

To build a real-time Big Data application, you need to consider several factors.

First– Can you process your Big Data in actual real-time, in order to get instant, relevant business insights? Batch processing can take too long for transactional data. This doesn’t mean that you don’t still rely on your batch processing in many ways…

Second – Can you preprocess and transform your data as it flows into the system, so that the relevant data is made digestible and routed to your batch processor, making batch more efficient as well. Finally, you also want to make sure the huge amounts of data you send to long-term storage are available for both batch processing and ad hoc querying, as needed.

XAP and Cassandra DB together can easily enable all the above to happen. With built-in event processing capabilities, full data consistency, and high-speed in-memory data access and local caching – XAP handles the real-time aspect with ease. Whereas, Cassandra is perfect for storing massive volumes of data, querying them ad hoc, and processing them offline.

Several hurdles had to be overcome to make the integration truly seamless and easy for end users – including XAP’s document-oriented model vs. Cassandra’s columnar data model, XAP’s immediate consistency (data must be able to move between models smoothly), XAP offers immediate consistency with performance, while Cassandra trades off between performance and consistency (with Cassandra as the Big Data store behind XAP processing, both consistency and performance are maintained).

Together with the Cassandra integration, XAP offers further enhancements. These include:

Data Grid Enhancements

To further optimize your queries over the data grid XAP now includes compound indices, which enable you to index multiple attributes. This way the grid scans one index instead of multiple indices to get query result candidates faster.
On the query side, new projections support enables you to query only for the attributes you’re interested in instead of whole objects/documents. All of these optimizations dramatically reduce latency and increase the throughput of the data grid in common scenarios.

The enhanced change API includes the ability to change multiple objects using a SQL query or POJO template. Replication of change operations over the WAN has also been streamlined, and it now replicates only the change commands instead of whole objects. Finally, a hook in the Space Data Persister interface enables you to optimize your DB SQL statements or ORM configuration for partial updates.

Visibility and Manageability Enhancements

A new web UI gives XAP users deep visibility into important aspects of the data grid, including event containers, client-side caches, and multi-site replication gateways.

Managing a low latency, high throughput, distributed application is always a challenge due to the amount of moving parts. The new enhanced UI helps users to maintain agility when managing their application.

The result is a powerful platform that offers the best of all worlds, while maintaining ease of use and simplicity.

Yaron Parasol is Director of Product Management for GigaSpaces, a provider of end-to-end scaling solutions for distributed, mission-critical application environments, and cloud enabling technologies.

Big Data, Cassandra, GigaSpace, GigaSpaces Technologies, NoSQL, XAP

GigaSpaces Releases XAP 9.5: Enhanced for Cassandra Big Data Store, .NET Framework

GigaSpaces Technologies has released XAP 9.5, a new version of its in-memory computing platform that enables a quick launch of high-performance real-time analytics systems for Big Data.

At the core of the latest release of the GigaSpaces platform is XAP 9.5’s enhanced integration with NoSQL datastores, such as Cassandra. Combining the Cassandra datastore with the GigaSpaces in-memory computing platform adds real-time processing and immediate consistency to the application stack, while also guaranteeing dynamic scalability and transactionality – all necessary elements for enterprises that need real-time analytics or processing of streaming Big Data.

In this combined architecture, XAP in-memory computing provide the real-time data processing engine that is interoperable with any language or application framework, while the Cassandra DB provides long-term storage of data for use in real-time analytics.

A GigaSpaces benchmark done for the integration of XAP with Cassandra shows that this integration dramatically improves real-time performance for data retrieval operations. Putting the GigaSpaces in-memory data grid in front of the Cassandra Big Data solution resulted in performance of read that is up to 2000 times faster.

Up until XAP 9.5. this integration was only available for XAP Java users. XAP 9.5 further innovates by allowing .Net users to leverage the same built in Cassandra integration. This integration provides a seamless bi-directional translation between Cassandra’s columnar data model and the richer document and object oriented models available in XAP. This works for both Java & .NET XAP deployments allowing for .NET developers to speed up their Cassandra based big data applications.

“The GigaSpaces XAP Cassandra integration enables companies to enjoy both in-memory data grid capabilities and Big Data processing, easily and for any framework – Java or .NET,” says Uri Cohen, GigaSpaces VP of Product. “This enables companies to be more agile in meeting both current and future data processing challenges.”