At the start of the big data journey, IT organizations often experiment with a sandbox or proof-of-concept project which, once successful, evolves into batch analytical big data before expanding toward real-time and operational uses of big data. The challenge for IT is to avoid a number of obstacles that have the potential to derail projects early on, which then lead to failed return-on-data results.
New Data Sources
Every organization abounds with data sources, many of them still untapped. One of the promises of big data is to enable access to all of these sources – and combine new data with existing data for new business insight. As a result, big data projects need to contend with systems as varied as ERPs and databases, Cloud and social media, data originating from the Internet of Things, and more.
The big data environment is a fast-evolving one, and legacy integration architectures fail to keep pace. Engine-based platforms that require deployment inside Hadoop simply don’t scale beyond proof-of-concept environments. Native integration with modern data platforms is required for efficiency and manageability.
Security and trustworthiness are two of the most commonly mentioned gaps in early big data deployments – and rightfully so. Big data projects often deal with sensitive personal information, or confidential enterprise records. Native support of Hadoop’s security protocols, and the ability to control and enforce data quality inside Hadoop, are the only solution to proper data governance. Alternative legacy integration options will vastly impact company performance.
There is a shortage of big data skills on the job market. Few developers master MapReduce, the ones who do demand premium packages, and once developers have been trained, retaining them becomes a challenge in itself. Being able to easily “upskill” existing resources so they can work with big data technologies can be a tremendous advantage.
Legacy integration platforms command pricing models that are impossible to reconcile with the agility and scalability required to run successful big data projects. Factoring in the cost of big data developers and the need for predictability only proves the need to revisit the choice of integration platform.
Today more than ever, IT needs to stay current with the latest technology changes and anticipate the demands from the business – demands that keep increasing for a higher return-on-data.
Continued use of legacy integration technology is severely impacting the potential of big data. IT must transition toward a future-proof integration platform that will bring agility and predictability to the delivery of big data projects – and deliver on the promise of big data.
Because of the unique constraints, but also unique benefits of big data, Talend equips IT to deliver the highest return-on-data to the business.
Native Big Data Support
Unlike legacy integration solutions, Talend natively resides in the Hadoop environment with a zero footprint deployment. Natively integrated with major Hadoop distributions such as Cloudera, Hortonworks and others, Talend also uses native Hadoop security, and performs native data quality inside Hadoop. This enables optimum business performance.
Like Hadoop, Talend is committed to open source and open standards and the benefits that they bring: an innovation ecosystem, no vendor lock-in, faster and more agile development, and support from a broad community. The use of standards and broad Talend ecosystem mean developers can easily adopt Talend, and Talend resources can be easily found on the market.
Talend’s no-runtime subscription pricing model allows to predictably scale data and projects, today and tomorrow, without having to scale the cost of the integration. Adherence to Java, eclipse and big data standards reduce project development and maintenance time, so operational costs are predictable as well.
As the big data journey moves from the sandbox to analytics and real-time/operational use cases, Talend uniquely delivers a complete and unified integration platform that addresses all needs, today and tomorrow.
Data-driven organizations need more and more access to data, at a faster and faster pace. For them, the big data journey promises great rewards, but the path to success is scattered with risks. Few IT organizations are equipped to meet the expectations from the business, because of the new demands on integration infrastructure.