Bigdata Lakes on AWS
Data Lake Best Practices

Data is a serious business advantage with both current and future. It is essentially important to store, manage and analyse data in an appropriate way. The main objective of Data Lake seeks is to overcome the challenge of enterprise-wide repository to store every type of data in the consolidated place. Also, it has to be made easily available to serve users and applications.

Best Practices For Data Lake Design
Keep your Data Warehouse:

At any rate in its beginning, a Data Lake ought not to be viewed as a substitution to the Enterprise Data Warehouse (in the event that one as of now exists). The main role of the Data Lake ought to be to give a situation where clients can undoubtedly get to, analysis and improve with any information without the hazard or dread of affecting Business as normal or Operational exercises. The Enterprise Data Warehouse can even now assume a vital part for Operational and Business as regular reporting – and in doing as such permit the Data Lake to unreservedly work as an instrument of advancement.

Work with ANY Data:

Prepare, Standardize and Store ANY Type of Data in its crude configuration Irrespective of starting point, structure or organization. As all information is put away in its crude frame, clients are enabled to go past the structures typically found in information stockrooms to investigate and reveal new reports/bits of knowledge.

Don’t worry about Structure or Schema:

The information lake is the place information lives as near its regular state as could reasonably be expected – the information structures and necessities don’t should be characterized until the information is required. Ought a Data Lake not at all like Data Warehouses, to not require pre-characterized outlines.

Use Decoupled Architectures & Separate Storage/Compute:

An efficient Data Lake design seeks to decouple storage and compute so the right analytics tools can be used (and paid for on a needs basis) at the right time based on the type of analysis required.

Build for Innovation and Experimentation:

Users should be able to get access to data they require, fast, to encourage innovation, experimentation and accelerated time to insight. Processes to access data should be well defined, clear and quick – and users should be able to choose from multiple analytical tools to support advanced analytics use cases that go beyond traditional BI.

Integrate with multiple Analytical Platforms:

Data lakes should be deployed in a healthy and complete technology ecosystem that enables users to turn Data from the lake into valuable insights, using multiple analytical platforms to support analysis of different data types and intelligence use cases (Streaming Data, non-relational data sets etc)

Judgment of a Data Lake to an Enterprise Data Warehouse
Data Lakes can be balancing to an EDW not a replacement.
Contain structured/semi-structured/unstructured data, EDW contains structured data only.
Have low cost of storage, EDW is expensive for high volumes
Responsive, fast incorporation of new content, EDW is less agile and time consuming with new content.
Used for Data Science/Predictive Analytics/BI use cases, EDW is used primarily for BI use cases.
Data is kept in the raw form and modelled only if required, EDW is modelled before loading.
Have loosely defined SLAs, EDW has tight SLAs and production schedules
Cloudcdc Professional Services for Data Lakes on Amazon Web Services

Amazon Web Services provides a varied range of professional technologies to build cost effective, accessible and high performance Data Lakes based on industry best practices. CloudCDC is an AWS Advanced partner with Big Data Competency.

CloudCDC provides Enterprise Grade professional services and customised solutions for high performance Data Lakes and analytics environments on Amazon Web Services. Our team is highly efficient and specialized in working across broader scopes of Amazon Web Services and tools to provide out of the box solutions.

Our objective is to bring fruitful results and goal achievements.

Contact us
Enterprise Grade Professional Services & Customized Solutions for Amazon Web Services

We specialize in designing, building and implementation of Analytics environments with a wide range of Amazon Web service technologies

We work to design and implement for unique requirements.

CloudCDC provides services to design and build comprehensive analytics structure by utilizing a wide array of AWS technologies Redshift, Lambda, AWS IoT, S3, Kinesis, DynamoDB, Hadoop on EMR and RDS. Our team end to end solutions to deliver defined business results.

Detailed consultation and support

We are experts in helping clients with the most effective and appropriate AWS Platform services and provide the perfect combination of out of the box technologies with customization. Even coding and development is provided wherever needed to expedite the project timelines and minimize the challenges.

Reasons to work with Cloudcdc – how are we different?
Contractually defined outcomes

We totally understand in delivering results with complete definition. We set genuine timelines and commit our clients to achieve what they desire. We wish to deliver success to our clients and achieve that extra milestone.”

Agility and minimal upfront investment

Experience an on-demand & cloud inspired engagement model with minimal upfront investments and a focus on rapid time to insight. Projects are divided into multiple micro stages and deliverables which gives our clients the flexibility to evaluate results and outcomes at each stage before participating further into identified solutions, technologies or platforms.

An impressive track record

CloudCDC has been in business since a while now and has one of the most experienced AWS and Redshift teams with a demonstrated track record on large enterprise deployments. We are proud of our team and deliver the best we can, everytime.

Some clients using our services
Key Services provided include:
Data Lake Build
Constructing solutions for Big Data Analytics
Requirements Analysis & Strategic Planning
Architecture Design & Optimisation
Data Migration & Integration
Data Warehouse Design
Data Transformation and Modelling
Rapid prototyping and POC exercises for platform selection
We are famous with clients for:
Building Data Lakes
Constructing solutions for Big Data Analytics
Building environments for Real Time Reporting and Analytics
Working with large,complex data sets

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