Technology Trends/Datalakes

Technology Trends / Datalakes Tendances Technologiques / Lac de Donnés
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Status	Translation
Initial release	May 5, 2019
Latest version	July 18, 2019
Official publication	Kubernetes.pdf
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Datalakes is a central system or repository of data that is stored in its natural/raw format. A datalake acts as a single store for all enterprise data. Data is transformed using machine learning, advanced,analytics, and visualization. Several forms of data can be hosed in a datalake. These include structured data from relational databases, unstructured data, semi-structured data, and binary data.

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Business Brief

In an ever-increasing hyperconnected world, corporations and businesses are struggling to deal with the responsibilities of storage, management and quick availability of raw data. To break these data challenges down further:

• Data comes in many different structures.
  • Unstructured
  • Semi-Sturctured
  • Structured
• Data comes from many disparate sources.
  • Enterprise Applications
  • Raw Files
  • Operation and Security Logs
  • Financial Transactions
  • Internet of Things (IoT) Devices and Network Sensors
  • Websites
  • Scientific Research
• Data sources are often geographically distributed to multiple locations
  • Datacenters
  • Remote Offices
  • Mobile Devices

In an effort to resolve these data challenges, a new way of managing data was created which drove data oriented companies to invent a new data storage mechanism called a Data Lake.

Data Lakes are characterized as:

• Collect Everything
  • A Data Lake contains all data; raw sources over extended periods of time as well as any processed data.
• Dive in anywhere
  • A Data Lake enables users across multiple business units to refine, explore and enrich data on their terms.
• Flexible Access
  • o A Data Lake enables multiple data access patterns across a shared infrastructure: batch, interactive, online, search, in-memory and other processing engines.

Data Lakes are essentially a technology platform for holding data. Their value to the business is only realized when applying data science skills to the lake.

To summarize, usecases for Data Lakes are still being discovered. Cloud providers are making it easier to procure Data Lakes and today Data Lakes are primarily used by Research Institutions, Financial Services, Telecom, Media, Retail, Manufacturing, Healthcare, Pharma, Oi l& Gas and Governments.

Technology Brief

The most popular implementation of a Data Lake is through the open source platform called Apache Hadoop. Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data and computation. Hadoop was originally created by researchers at Google as a storage method to handle the indexing of websites on the Internet; At that time it was called the Google File System.

A Data Lake is a centralized repository that allows you to store all your structured and unstructured data at any scale. You can store your data as-is, without having to first structure the data, and run different types of analytics—from dashboards and visualizations to big data processing, real-time analytics, and machine learning to guide better decisions.”

Data can flow into the Data Lake by either batch processing or real-time processing of streaming data. Additionally, data itself is no longer restrained by initial schema decisions and can be exploited more freely by the enterprise. Rising above this repository is a set of capabilities that allow IT to provide Data and Analytics as a Service (DAaaS), in a supply-demand model. IT takes the role of the data provider (supplier), while business users (data scientists, business analysts) are consumers.

The DAaaS model enables users to self-serve their data and analytic needs. Users browse the lake’s data catalog (a Datapedia) to find and select the available data and fill a metaphorical “shopping cart” (effectively an analytics sandbox) with data to work with. Once access is provisioned, users can use the analytics tools of their choice to develop models and gain insights. Subsequently, users can publish analytical models or push refined or transformed data back into the Data Lake to share with the larger community.

Although provisioning an analytic sandbox is a primary use, the Data Lake also has other applications. For example, the Data Lake can also be used to ingest raw data, curate the data, and apply Export-Transform-Load (ETL). This data can then be loaded to an Enterprise Data Warehouse. To take advantage of the flexibility provided by the Data Lake, organizations need to customize and configure the Data Lake to their specific requirements and domains.

Industry Usage

There are a variety of ways Data Lakes are being used in the industry:

• Ingestion of semi-structured and unstructured data sources (aka big data)such as equipment readings, telemetry data, logs, streaming data, and so forth. A Data Lake is a great solution for storing IoT (Internet of Things) type of data which has traditionally been more difficult to store, and can support near real-time analysis. Optionally, you can also add structured data (i.e., extracted from a relational data source) to a Data Lake if your objective is a single repository of all data to be available via the lake.

• Experimental analysis of data before its value or purpose has been fully defined. Agility is important for every business these days, so a Data Lake can play an important role in "proof of value" type of situations because of the "ELT" approach discussed above.

• Advanced analytics support. A Data Lake is useful for data scientists and analysts to provision and experiment with data.

• Archival and historical data storage. Sometimes data is used infrequently, but does need to be available for analysis. A Data Lake strategy can be very valuable to support an active archive strategy.

• Distributed processing capabilities associated with a logical data warehouse.

 How TD Bank Made Its Data Lake More Usa

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Toronto-Dominion Bank (TD Bank) is one of the largest banks in North America, with 85,000 employees, more than 2,400 locations between Canada and the United States, and assets nearing $1 trillion. In 2014, the company decided to standardize how it warehouses data for various business intelligence and regulatory reporting functions. The company purchased a Hadoop distribution and set off to build a large cluster that could function as a centralized lake to store data originating from a variety of departments.

Canadian Government Use

In 2019, the Treasury Board of Canada Secretariat (TBS), partnered with Shared Services Canada and other departments, to identify a business lead to develop a Data Lake (a repository of raw data) service strategy so that the GC can take advantage of big data and market innovation to foster better analytics and promote horizontal data-sharing.

Big data is the technology that stores and processes data and information in datasets that are so large or complex that traditional data processing applications can’t analyze them. Big data can make available almost limitless amounts of information, improving data-driven decision-making and expanding open data initiatives. Business intelligence involves creating, aggregating, analyzing and visualizing data to inform and facilitate business management and strategy. TBS, working with departments, will lead the development of requirements for an enterprise analytics platform.

Data Lake development in the GC is a more recent initiative. This is mainly due to the GC focussing resources on the implementation of cloud initiatives. However, there are some GC departments engaged in developing Data Lake environments in tandem to cloud initiatives.

Notably, the Employment and Social Development Canada (ESDC) is preparing the installment of multiple Data Lakes in order to enable a Data Lake Ecosystem and Data Analytics and Machine Learning toolset. This will enable ESDC to share information horizontally both effectively and safely, while enabling a wide variety of data analytics capabilities. ESDC aims to maintain current data and analytics capabilities up-to-date while exploring new ones to mitigate gaps and continuously evolve our services to meet client’s needs.

Implications for Government Agencies

Shared Services Canada (SSC)

Value Proposition

There are three common value propositions for pursuing Data Lakes. 1) It can provide an easy and accessible way to obtain data faster; 2) It can create a singular inflow point of data to help connect and merge information silos in an organization; and 3) It can provide an experimental environment for experienced data scientists to enable new analytical insights.

Data Lakes can provide data to consumers more quickly by offering data in a more raw and easily accessible form. Data is stored in its native form with little to no processing, it is optimized to store vast amounts of data in their native formats. By allowing the data to remain in its native format, a much timelier stream of data is available for unlimited queries and analysis. A Data Lake can help data consumers bypass strict data retrieval and data structured applications such as a data warehouse and/or data mart. This has the effect of improving a business’ data flexibility.

Some companies have in fact used Data Lakes to replace existing warehousing environments where implementing a new data warehouse is more cost prohibitive. A Data Lake can contain unrefined data, this is helpful when either a business data structure is unknown, or when a data consumer requires access to the data quickly.

A Data Lake is not a single source of truth. A Data Lake is a central location in which data converges from all data sources and is stored, regardless of the data formatting.

As a singular point for the inflow of data, sections of a business can pool their information together in the Data Lake and increase the sharing of information with other parts of the organization. In this way everyone in the organization has access to the data. A Data Lake can increase the horizontal data sharing within an organization by creating this singular data inflow point. Using a variety of storage and processing tools analysts can extract data value quickly in order to inform key business decisions.

A Data Lake is optimized for exploration and provides an experimental environment for experienced data scientists to uncover new insights from data. Analysts can overlay context on the data to extract value. All organizations want to increase analytics and operational agility.

The Data Lake architectural approach can store large volumes of data, this can be a way in which cross-cutting teams can pool their data in a central location and by complementing their systems of record with systems of insight.

Data Lakes present the most potential benefits for experienced and competant data scientists.

Having structured, unstructured and semistructured data, usually in the same data set, can contain business, predictive, and prescriptive insights previously not possible from a structured platform as observed in data warehouses and data marts.

Challenges

The greatest challenge in regards to Kubernetes is its complexity. However, security, storage and networking, maturity, and competing enterprise transformation priorities are also challenges facing the Kubernetes technology.

Kubernetes Complexity and Analyst Experience

There is the challenge of a lack of organizational and analyst experience with container management and in using Kubernetes. Managing, updating, and changing a Kubernetes cluster can be operationally complex, more so if the analysts have a lack of experience. The system itself does provide a solid base of infrastructure for a Platform as a Service (PaaS) framework, which can reduce the complexity for developers. However, testing within a Kubernetes environment is still a complex task. Although its use cases in testing are well noted, testing several moving parts of an infrastructure to determine proper application functionality is still a more difficult endeavour . This means a lot of new learning will be needed for operations teams developing and managing Kubernetes infrastructure. The larger the company, the more likely the Kubernetes user is to face container challenges.

Security

In a distributed, highly scalable environment, traditional and typical security patterns will not cover all threats. Security will have to be aligned for containers and in the context of Kubernetes. It is critical for operations teams to understand Kubernetes security in terms of containers, deployment, and network security. Security perimeters are porous, containers must be secured at the node level, but also through the image and registry. Security practices in the context of various deployment models will be a persistent challenge.

Storage & Networking

Storage and networking technologies are pillars of data center infrastructure, but were designed originally for client/server and virtualized environments. Container technologies are leading companies to rethink how storage and networking technologies function and operate. Architectures are becoming more application-oriented and storage does not necessarily live on the same machine as the application or its services. Larger companies tend to run more containers, and to do so in scaled-out production environments requires new approaches to infrastructure.

Some legacy systems can run containers and only sometimes can VMs can be replaced by containers. There may be significant engineering consequences to existing legacy systems if containerization and Kubernetes is implemented in a legacy system not designed to handle that change. Some Legacy systems may require refactoring and making it more suitable for containerization. Some pieces of a system may be able to be broken off and containerized. In general, anything facing the internet should be run in containers.

Maturity

Kubernetes maturity as a technology is still being tested by organizations. For now, Kubernetes is the market leader and the standardized means of orchestrating containers and deploying distributed applications. Google is the primary commercial organization behind Kubernetes; however they do not support Kubernetes as a software product. It offers a commercial managed Kubernetes service known as GKE but not as a software. This can be viewed as both a strength and a weakness. Without commercialization, the user is granted more flexibility with how Kubernetes can be implemented in their infrastructure; However, without a concrete set of standards of the services that Kubernetes can offer, there is a risk that Google’s continuous support cannot be guaranteed. Its donation of Kubernetes code and intellectual property to the Cloud Native Computing Foundation does minimize this risk since there is still an organization enforcing the proper standards and verifying services Kubernetes can offer moving forward . It is also important to note that the organizational challenges that Kubernetes users face have been more dependent on the size of the organization using it.

Kubernetes faces competition from other scheduler and orchestrator technologies, such as Docker Swarm and Mesosphere DC/OS. While Kubernetes is sometimes used to manage Docker containers, it also competes with the native clustering capabilities of Docker Swarm. However, Kubernetes can be run on a public cloud service or on-premises, is highly modular, open source, and has a vibrant community. Companies of all sizes are investing into it, and many cloud providers offer Kubernetes as a service.

Competing Enterprise Transformation Priorities

The last challenge facing Kubernetes initiative development and implementation is its place in an organization’s IT transformation priority list. Often there are many higher priority initiatives that can take president over Kubernetes projects.

Considerations

Strategic Resourcing and Network Planning

A strategic approach to Kubernetes investments will need to be developed to ensure opportunities are properly leveraged. The GC invests a significant portion of its annual budget on IT and supporting infrastructure. Without strategic Kubernetes direction the fragmented approaches to IT investments, coupled with rapid developing technology and disjointed business practices, can undermine effective and efficient delivery of GC programs and services. A clear vision and mandate for how Kubernetes will transform services, and what the end-state Kubernetes initiative is supposed to look like, is a prominent consideration.

SSC should consider defining a network strategy for Kubernetes adoption. Multiple factors should be taken into account, including the amount of resources, funding, and expertise that will be required for the development and experimentation with Kubernetes technologies. Calculation of resource requirements including CPU, memory, storage, etc. at the start of Kubernetes projects is imperative. Considerations include whether or not an in-house Kubernetes solution is required or if a solution can be procured. Other strategy considerations include analyzing different orchestration approaches for different application use cases.

Complexity and Skills Gap

Kubernetes is a good technology and the de facto standard for orchestrating containers, and containers are the future of modern software delivery. But it is notoriously complex to manage for enterprise workloads, where Service Level Agreements (SLAs) are critical. The operational pain of managing production-grade Kubernetes is further complicated by the industry-wide talent scarcity and skills gap. Most organizations today struggle to hire Kubernetes experts, and even these “experts” lack advanced Kubernetes experience to ensure smooth operations at scale. SSC will need to be cautious in implementing Kubernetes and having the right staff experienced and comfortable in its use.

Customization and Integration Still Required

Kubernetes technology and ecosystem are evolving rapidly, because of its relatively new state, it is hard to find packaged solutions with complete out-of-the-box support for complex, large-scale enterprise scenarios. As a large and sophisticated enterprise organization, SSC will need to devote significant resources on customization and training. Enterprise Architecture pros will need to focus on the whole architecture of cloud-native applications as well as keep a close watch on technology evolution and industry.

Implementation usually takes longer than expected, however the consensus in the New Stack’s Kubernetes User Experience Survey is that Kubernetes reduces code deployment times, and increases the frequency of those deployments. However, in the short run, the implementation phase does consume more human resources. Additionally, implementation takes longer than expected. The consensus is that Kubernetes reduces code deployment times, and increases the frequency of those deployments. However, in the short run, the implementation phase does consume more human resources.

Pilot Small and Scale Success

SSC may wish to consider evaluating the current Service Catalogue in order to determine where Kubernetes can be leveraged first to improve efficiencies, reduce costs, and reduce administrative burdens of existing services as well as how a new Kubernetes service could be delivered on a consistent basis. Any new procurements of devices or platforms should have high market value and can be on-boarded easily onto the GC network. SSC should avoid applying in-house Kubernetes for production mission-critical apps. Failure of in-house deployments is high and thus should be avoided. SSC should pilot and establish a Kubernetes test cluster. With all new cloud-based technologies, piloting is preferred. Focus should first be on a narrow set of objectives and a single application scenario to stand up a test cluster.

Implement Robust Monitoring, Logging, and Audit Practices and Tools

Monitoring provides visibility and detailed metrics of Kubernetes infrastructure. This includes granular metrics on usage and performance across all cloud providers or private data centers, regions, servers, networks, storage, and individual VMs or containers. Improving data center efficiency and utilization on both on-premises and public cloud resources is the goal. Additionally, logging is a complementary function and required capability for effective monitoring is also a goal. Logging ensures that logs at every layer of the architecture are all captured for analysis, troubleshooting and diagnosis. Centralized, distributed, log management and visualization is a key capability. Lastly, routine auditing, no matter the checks and balances put in place, will cover topics that normal monitoring will not cover. Traditionally, auditing is as a manual process, but the automated tooling in the Kubernetes space is quickly improving.

Security

Security is a critical part of cloud native applications and Kubernetes is no exception. Security is a constant throughout the container lifecycle and it is required throughout the design, development, DevOps, and infrastructure choices for container-based applications. A range of technology choices are available to cover various areas such as application-level security and the security of the container and infrastructure itself. Different tools that provide certification and security for what goes inside the container itself (such as image registry, image signing, packaging), Common Vulnerability Exposures/Enumeration (CVE) scans, and more.. SSC will need to ensure appropriate security measures are used with any new Kubernetes initiatives, including the contents of the containers being orchestrated.

References