Friday, December 22, 2017

The Big Data Architecture Roadmap - An Incremental Approach - Part 2

In the first part of this blog series, I discussed how the hype around analytics powered by big data has glossed over the critical enablers and hard work necessary to fulfill that promise. I also discussed how a clear analytical capability divide currently separates the industry in terms of the critical capabilities requisite for analytical success. I recognized the need for an incremental roadmap to bridge the capability gap as opposed to the prevalent ‘big bang’ approach, where on one hand, the diverse degree of implementation complexity of the big data use cases is not acknowledged, and on the other hand, massive investments are made in attempt to build the foundation in data lakes without a discernable ROI in use cases.
In this second part of the blog, I refer to the emerging data lake architecture patterns to propose an incremental data induction pattern. Based on that progression, I will propose a reference architecture that preserves current investment and supports gradual advancement, implementing use cases involving more complex and varied types of data, incrementally producing more and more impactful results.


Choice of use cases: Are big data and analytics necessarily combined?


Big data, Hadoop, and analytics have become almost synonymous in today’s parlance, and success with analytics is the refrain of today’s IT success. But, big data programs do not need to necessarily start with advanced analytics. Big data and Hadoop have many other operational use cases that are more technical in nature, and therefore, less impacted by the daunting impediments discussed in the previous blog. They provide an excellent opportunity for organizations to start on their big data voyage with much less capability.
Data warehouse (DW) off-loading is one such use case. Here, the Hadoop-based data lake is emerging as a natural fit for the huge detail transactional data sets that are being relocated to the data lake as organizations are modernizing their data warehouse in a multiplatform data warehouse environment (DWE). The data lake, with its linearly scalable architecture, reduces expensive storage and computation resources of the DWs and enables discovery-oriented exploration and analytics on these huge data sets in the Hadoop platform—capabilities that business and data analysts are pining for today.

A ‘big bang’ data lake program invites the risk of failing: populating it with the entire enterprise data irrespective of valid use cases will have poor ROI and present extreme governance and data management challenges.

TDWI research on data lake use cases supports this hypothesis. While 49% of the respondents understandably mentioned advanced analytics as their use case for their data lakes, another 49% mentioned data exploration and discovery, followed by 39% as extension of their data warehouse, 36% as staging for their data warehouse, and 36% as data warehouse off-load and cost reduction.
TCS has implemented many such strong technical use cases for organizations, establishing the new, modernized, extended multiplatform DWE architecture with the least disruption and most effect. In fact, if you are early in the big data adoption cycle, implementing these use cases is the only realistic way to start building the requisite capability. The decision-making culture, the business alignment, the data management, and the in-house technical skills come with practice rather than analysis. In that sense, these more technical and tactical use cases become the stepping stone for most organizations to begin their analytics journey. We will take up this thought in discussing the roadmap in the next blog.


Rethinking the ‘big bang’ approach to big data: An alternative to overflowing your data lake


Industry research on the types of data populating data lakes accords with the above proposition. According to a TDWI survey, the exclusive management of big data and other non-traditional data is still a minority practice for data lakes (15%), whereas managing mostly traditional data is the majority practice (45%).
According to another TDWI survey, 92% of 473 respondents are managing structured data, 65% are storing legacy data, 55% demographic and other third-party data, 38% application logs, and 35% are storing data in semi structured data. More exotic data types, IoT and unstructured data, seem to be lagging at 6% and 12% only.
TCS’ practice also indicates that organizations are most successful when adopting a natural progression starting from internal structured data and gradually ingesting increased complexity in terms of volume, velocity, and variety (3Vs), and in that order. This observation is supported by the industry research above. This incremental approach allows for the organization to build up the requisite advancement in capabilities in terms of technical skills, induction of domain expertise, enhanced data management processes, and requisite organization change associated with the more and more disruptive consumption of the analytic insights distilled from the increasingly complex data in the 3Vs.
A ‘big bang’ data lake program invites the risk of failing: populating it with the entire enterprise data irrespective of valid use cases will have poor ROI and present extreme governance and data management challenges. Building an enterprise data lake demands data-driven management culture, technology investments, new decision procedures, redesigned roles, and expertise that is costly and takes time to develop. Bridging the capability chasm here too is an incremental affair. I will take up this train of thought in the roadmap definition.


The big data architecture pattern: A multiplatform DWE for gradual complexity and maximum ROI


The diversity of data types and workload processing is driving today’s multiplatform DWE architectures. It gives users options so they can choose a platform with the storage, performance, and price characteristics that match a given data type or workload.
A recent TDWI report revealed that 17% of surveyed data warehouse programs already have Hadoop in production alongside a relational data warehouse environment, where the relational data warehouse and the Hadoop-based data lake coexist with tight integration and complement each other. That’s because the strengths of one compensates for the weaknesses of the other. They simply provide different sets of functions, thereby giving organizations twice the options.
Also, in terms of usage of data, these two platforms play complementary roles. For example, financial reports that demand accuracy down to the penny and a lineage that’s unassailable in an audit will remain in the data warehouses. That’s why the relational enterprise data warehouses (EDWs) still remain strongly relevant today. Here, the data elements, their relationships, and derivations that are mostly very complex are understood completely beforehand. As opposed to that pattern, early ingestion and the data prep practices that go with it are more appropriate for discovery analytics, and they tend to be the top priority for a data lake. The outputs of such analytics are, by nature, estimates and generalizations (e.g., customer segments and entity clusters suggesting fraud) as against the requisite accuracy financial reports, according to the report “TDWI Checklist Report: Emerging Best Practices for Data Lakes.”
Naturally, the simple DWE has now become the systems architecture norm which includes a central EDW with a few additional data platforms, and it will continue to be the norm for some years according to TDWI research. The architectural complexity of a DWE will increase with progressive induction of more big data types. DWEs will start simple with a handful of platforms, evolving into complex DWEs, integrating a dozen or more, where newer platforms will be added to induct more and more velocity and variety of data.
Recommended Reference Architecture
The diagram above is TCS’ recommendation of the modern hybrid, integrated, and multiplatform data warehouse environment, and its data flows at a high level. This architectural pattern off-loads exploration, exploitation, storage, and processing of high-volume structured, semi structured, and unstructured data to its Hadoop layer, and leaves the complex processing of ‘small’ data to the relational layer, which it does best.
This architecture avoids the expensive and time-consuming step of copying the entire enterprise data to the data lake—that step is redundant with big data connectors being available for all established relational databases. These connectors allow for an analytical data flow or an ETL process to access both data stores seamlessly. This architecture also keeps sensitive data within an organization’s secure enterprise storage systems—security and governance on the Hadoop layer would need to be applied on individual relational data sets being copied over on a use-case basis, providing easier control.
This architecture also protects the investment in the relational data warehouses and makes their use to the fullest extent in the new environment. It reduces risk with the least disruption in the existing implementations, and provides the best ROI by reducing unnecessary investments in storing enterprise data sets that are best left where they are at present.
Towards the roadmap


In terms of the roadmap for big data and analytics, their use cases should have increasing demands in the dimensions of implementation capability and capacity needed, as well as the degree of organizational change required. The roadmap should start with technical use cases that require the least additional skill and have a positive, if minimal, impact to the business processes. The roadmap then gradually evolves into use cases that demand more internal capability and capacity and have wider business impact, in the final stage being adopted for use in the organization’s strategic planning.
In the next blog, I will expand on a four-phase analytics strategy and roadmap. I will outline a progressive approach that expands in big data implementation capability complexity and the degree of organizational change involved in their intrusiveness on current business processes.

Thanks to Suman Ghosh @TCS for Enlightening us with this Article.




K@run@

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