Characteristics of a Successful Data Lake Implementation
A Data Lake enables users to analyze the full variety and volume of data stored in the lake. This necessitates features and functionalities to secure and curate the data, and then to run analytics, visualization, and reporting on it. The characteristics of a successful Data Lake include:
- Use of multiple tools and products. Extracting maximum value out of the Data Lake requires customized management and integration that are currently unavailable from any single open-source platform or commercial product vendor. The cross-engine integration necessary for a successful Data Lake requires multiple technology stacks that natively support structured, semi-structured, and unstructured data types.
- Domain specification. The Data Lake must be tailored to the specific industry. A Data Lake customized for biomedical research would be significantly different from one tailored to financial services. The Data Lake requires a business-aware data-locating capability that enables business users to find, explore, understand, and trust the data. This search capability needs to provide an intuitive means for navigation, including key word, faceted, and graphical search. Under the covers, such a capability requires sophisticated business ontologies, within which business terminology can be mapped to the physical data. The tools used should enable independence from IT so that business users can obtain the data they need when they need it and can analyze it as necessary, without IT intervention.
- Automated metadata management. The Data Lake concept relies on capturing a robust set of attributes for every piece of content within the lake. Attributes like data lineage, data quality, and usage history are vital to usability. Maintaining this metadata requires a highly-automated metadata extraction, capture, and tracking facility. Without a high-degree of automated and mandatory metadata management, a Data Lake will rapidly become a Data Swamp.
- Configurable ingestion workflows. In a thriving Data Lake, new sources of external information will be continually discovered by business users. These new sources need to be rapidly on-boarded to avoid frustration and to realize immediate opportunities. A configuration-driven, ingestion workflow mechanism can provide a high level of reuse, enabling easy, secure, and trackable content ingestion from new sources.
- Integrate with the existing environment. The Data Lake needs to meld into and support the existing enterprise data management paradigms, tools, and methods. It needs a supervisor that integrates and manages, when required, existing data management tools, such as data profiling, data mastering and cleansing, and data masking technologies.
Keeping all of these elements in mind is critical for the design of a successful Data Lake.
Designing the Data Lake
Designing a successful Data Lake is an intensive endeavor, requiring a comprehensive understanding of the technical requirements and the business acumen to fully customize and integrate the architecture for the organization’s specific needs.
Hereafter the fields of expertise in which Big Data Scientists and Engineers will assist you to provide the expertise necessary to evolve the Data Lake to a successful Data and Analytics as a Service solution, including:
- DAaaS STRATEGY Service Definition. Informationists will help you define the catalog of services to be provided by the DAaaS platform, including data onboarding, data cleansing, data transformation, datapedias, analytic tool libraries, and others.
- DAaaS ARCHITECTURE. You'll need to achieve a target-state DAaaS architecture, including architecting the environment, selecting components, defining engineering processes, and designing user interfaces.
- DAaaS PoC. You'll need to execute Proofs-of-Concept (PoC) to demonstrate the viability of the DAaaS approach. Key capabilities of the DAaaS platform are built/demonstrated using leading-edge bases and other selected tools.
- DAaaS OPERATING MODEL DESIGN and ROLLOUT. You'll need to customize your DAaaS operating model to meet your individual processes, organizational structure, rules, and governance. This includes establishing DAaaS chargeback models, consumption tracking, and reporting mechanisms.
- DAaaS Platform CAPABILIY BUILD-OUT. You'll need to conduct an iterative build-out of all platform capabilities, including design, development and integration, testing, data loading, metadata and catalog population, and rollout.
The Data Lake can be an effective data management solution for advanced analytics experts and business users alike. A Data Lake allows users to analyze a large variety and volume when and how they want. Following a Data and Analytics as a Service (DAaaS) model provides users with on-demand, self-serve data.
However, to be successful, a Data Lake needs to leverage a multitude of products while being tailored to the industry and providing users with extensive, scalable customization.
- If you need or want to implement a data lake for your business in Europe .I can help you get IT professionals to provide a blend of technical expertise and business acumen to help your organizations design and implement their perfect Data Lake.
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