Collaborative work can sometimes yield great benefits.
The Big Data market is continuously growing and in order to gain a bigger market share many companies are looking to collaborations.
Often the collaborative approach is based upon a sharing of technologies, or using one company’s technology to make the other stronger.
The benefit may be that combined these companies have a larger market reach, and sometimes, two heads are better than one.
CBR has compiled a list of some of the more significant Big Data collaborations.
1. IBM and Cloudera
Cloudera, an Apache Hadoop data management software provider partnered with IBM a number of years ago, with the purpose of integrating Apache Hadoop and Cloudera Manager, with IBM’s Big Data platform.
The partnership was designed to enable customers to take advantage of an application which will included a feature complete Hadoop platform. The features would include, BI capabilities and connectors to IBM Netezza, IBM DB2, IBM InfoSphere Warehouse and IBM Smart Analytics System.
Further collaborative systems have been built in recent years such as, a multi-server SoftLayer infrastructure system.
2. Huawei and SAP
This partnership was updated with great fanfare at CeBIT 2015. The, ‘Document of Understanding’ committed the companies to a number of co-ventures. However, the companies have had similar partnerships in place before, with a technology partnership in place since 2012.
One of the most recent developments from the partnership saw Huawei introducing its FusionCube for SAP HANA solution, which looked to tackle data from Sinopec.
As Huawei continues to grow, the collaboration between the two companies looks as though it will be a fruitful one, with SAP benefitting from Huawei’s power in the Chinese market.
3. Cloudera, Dell and Intel
The purpose of the partnership was to launch a dedicated Dell In-Memory Appliance for Cloudera Enterprise.The goal was to accelerate the deployment of Apache Hadoop in enterprises.
This is a partnership which looked to both utilise the technical expertise of each companies and also the combined global reach, to help make a bigger impact on the global market.
Technology from Intel, with the use of the Intel Xeon processor was used with technology from Dell with the R920 hardware system, this allowed the appliance to scale up from a single system to an unlimited number of nodes.
Intel Big Data Solutions vice president and general manager Ron Kasabian said: "The innovative use of memory as well as CPU in large distributed systems is essential to the performance and scalability required by real-time analytics of streaming data."
4. Accenture and Hortonworks
The partnership has seen a few formal agreements through the years, but the purpose of the collaboration has remained largely the same. Which is to make structured and unstructured data more accessible to users.
This is another Apache Hadoop collaboration, which is quite common, however, the significance of the partnership is the size and market reach for both companies.
In 2014, Accenture’s senior managing director Narendra Mulani, said: "As companies move toward becoming digital businesses, they need to analyse all of their data to find key insights that will drive business outcomes."
"Our alliance with Hortonworks underpins our commitment to help clients embrace the challenges of digital transformation and accelerate returns from their data and analytics investments."
5. SAP and SAS
SAP and SAS teamed up to combine SAS analytics onto the SAP HANA platform. HANA software was incorporated into SAS business applications and SAS’s analytics were able to run on the HANA platform, an alliance which benefits both companies.
The goals of the partnership were to eliminate data movement, duplication and reconciliation, while simplifying IT and reduce costs.
The solution was targeted at customer intelligence, risk management, asset management and anti-money laundering industries.
Jim Goodnight, SAS CEO, said: "Between our two companies, we have the expertise and the products to help ensure that our customers can see and act on the power of performing advanced data analysis within their database and not outside of it."