Difference between revisions of "Technology Trends/Datalakes"
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<li><p><b>Distributed processing </b>capabilities associated with a logical data warehouse.</p></li> | <li><p><b>Distributed processing </b>capabilities associated with a logical data warehouse.</p></li> | ||
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− | <p class="expand mw-collapsible-content"><b | + | <p class="expand mw-collapsible-content"><b>[[https://www.datanami.com/2017/10/03/td-bank-made-data-lake-usable How TD Bank Made Its Data Lake More Usa]]</b></p> |
− | + | <p class="expand mw-collapsible-content">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.</p> | |
<h2>Canadian Government Use</h2> | <h2>Canadian Government Use</h2> |
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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.
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 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
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.”
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.
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.
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[1].
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.
Implications for Government Agencies
Value Proposition
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.
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.
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.
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
Although Data Lake technology has many benefits for organizations dealing with big data it has its own challenges. For example:
Data Governance and Semantic Issues
Often, Data Lake implementations are focused on storing data instead of managing the data. Data Lakes are not optimized for semantic enforcement or consistency. They are made for semantic flexibility, to allow anyone to provide context to data if they have the skills to do so.
Putting data in the same place does not remove it’s ambiguity or meaning. Data Lakes provide unconstrained, “no compromises” storage model environment without the data governance assurances common to data warehouses or data marts. Proper meta data is essential for a Data Lake, without appropriate meta data the Data Lake will not work as intended. It is beneficial to think of meta data as the fish finder in the Data Lake.
Lack of Quality and Trust in Data
Data Swamps, Performance, and Flexibility Challenges
A Data Lake is not optimized for a high number of users or diverse and simultaneous workloads due to intensive query tasks. This can result in performance degradation and failures are common when running extractions, transformations, and loading tasks all at the same time. On-premises Data Lakes face other performance challenges in that they have a static configuration.
Data Hoarding and Storage Capacity
The storage capacity of all this data (and future data) will be an ongoing challenge and one that will require constant management. While Data Lakes can and will be stored on the cloud, SSC as cloud broker for the GC will need to provide the appropriate infrastructure and scalability to clients.
Advanced Users Required
Data Security
Data in a Data Lake lacks standard security protection with a relational database management system or an enterprise database. In practice, this means that the data is unencrypted and lacks access control.Considerations
Data Lakes should not be confused for conventional databases although they both store information. A Data Lake will always underperform when tasked with the jobs of a conventional database.
Creating a Data Lake becomes the goal rather than achieving a strategic objective.
SSC should be cognisant that there are significant overinflated expectations revolving around Data Lakes. Inflated expectations lead to vague and ambiguous use cases and increased chances of catastrophic failures. As a Service Provider, SSC must be strict in establishing clear goals for Data Lake provision efforts before deployment. SSC, should be wary of attempts to replace strategy development with infrastructure. A Data Lake can be a technology component that supports a data and analytics strategy, but it cannot replace that strategy.
Design Data Lakes with the elements necessary to deliver reliable analytical results to a variety of data consumers. The goal is to increase cross-business usage in order to deliver advanced analytical insights. Build Data Lakes for specific business units or analytics applications, rather than try to implement some vague notion of a single enterprise Data Lake. However, alternative architectures, like data hubs, are often better fits for sharing data within an organization.
References
- ↑ Treasury Board of Canada Secretariat. (March 29th, 2019). Digital Operations Strategic Plan: 2018-2022. Government of Canada. Treasury Board of Canada Secretariat. Retrieved 26-May-2019 from: [1]
- ↑ Ibid.
- ↑ Brisson, Yannick, and Craig, Sheila. (November, 2018). ESDC Data Lake – Implementation Strategy and Roadmap Update. Government of Canada. Employment and Social Development Canada – Data and Analytics Services. Presentation. Last Modified on 2019-04-26 15:45. Retrieved 07-May-2019 from GCDocs[2]
- ↑ Dennis, A. L. (2018, October 15). Data Lakes 101: An Overview. Retrieved from [3]
- ↑ Marvin, R., Marvin, R., & Marvin, R. (2016, August 22). Data Lakes, Explained. Retrieved from [ https://www.pcmag.com/article/347020/data-lakes-explained]
- ↑ The Data Lake journey. (2014, March 15). Retrieved from [4]
- ↑ Google File System. (2019, July 14). Retrieved from [5]
- ↑ Coates, M. (2016, October 02). Data Lake Use Cases and Planning Considerations. Retrieved from [6]
- ↑ Bhalchandra, V. (2018, July 23). Six reasons to think twice about your data lake strategy. Retrieved from [7]
- ↑ Data Lake Expectations: Why Data Lakes Fail. (2018, September 20). Retrieved from [8]
- ↑ Data Lake: AWS Solutions. (n.d.). Retrieved from [9]