Why Implement an AWS Data Lake?
Realize optimal business value within a data-driven culture by implementing an AWS Data Lake. Utilize a consistent and robust approach to acquire vast amounts of data. By combining it with a thorough understanding of data, facilitated by modern Data Governance practices, we make data truly usable and simplify your data-backed decision making.
Take advantage of practically unlimited storage scaling.
Intelligently and dynamically change the storage class of certain data files to drastically reduce the storage cost.
AWS provides a number of services to allow for easily ingesting any data into the data lake.
Leverage the built-in AWS security mechanisms to meet compliance and legislation requirements.
Easy access to data
Queries to raw and curated data can be executed for shortest time to insights.
AWS provides the ability to a much larger number of people in your organization to benefit from extracting business value from the data in the data lake.
10x Increase in Analytics Team Productivity with an AWS Data Lake Implementation
more productive analytics team
manual effort needed to produce unified and consolidated reports
infrastructure maintenance needed
A North American Health group was struggling with consolidating their accounting reporting as the group consists of a number of companies and clinics each of them using different accounting software solutions. The group was also looking at a centralized repository for storing and reporting on their EMR data (Electronic Medical Records).
Approach to AWS Data Lake Implementation
A common perception is that migrating existing workloads to the public cloud, especially those with a lot of data, is complex, time consuming, and risky. However, choosing the right partner can help you establish best cloud practices to accelerate the process and lower risk.
- Identify all stakeholders.
- Conduct a series of exploratory workshops to get acquainted with the organization’s data strategy and long-term plans.
- Create a catalogue of the requirements to the data lake.
- Create a high-level design of the solution, making sure it integrates well with existing environments, while taking into consideration the possibility of future cloud migrations.
- Create an end-to-end implementation plan, defining scope, timelines, milestones, and deliverables.
- Define data ingestions strategies for all sources in scope.
- Optionally, if you plan to expand your activities in the cloud beyond the data lake, we can help you create a roadmap.
- If this is your first cloud project – our team will help you establish all necessary, cloud-based infrastructure and security mechanisms.
- Implement data pipelines to ingest data from any identified source and process the raw data into a standardized and efficient data format, allowing for further cost savings
- Configure CI/CD pipelines to automate testing and deployment.
- Deliver detailed technical documentation which will allow your team to run the data lake
- Conduct knowledge transfer and training sessions, making sure all technical and business users are well-acquainted with the delivered data lake solution.
Strategy alignment and roadmap
Identify what are your data strategy, cloud maturity and environment. Based on the findings, we not only design your data lake solution as per your requirements, but also create a roadmap for future development and advanced usage of the data lake across the entire organization.
Leverage our experienced team of seasoned professionals to create the data lake and necessary data pipelines for you, establish CI/CD pipelines, and make sure all security mechanisms are in place. Tap into Adastra’s expertise and benefit from frameworks, based on best practices and implemented in numerous other successful deliveries.
We make sure your team is fully capable of managing the implemented data lake and is comfortable working with it. Optionally, you can benefit from Adastra’s Managed Services where we run and evolve the data lake for you.
A data lake is a system of technologies that allow for the ingestion, storage, management and querying of batch and streaming data at tremendous scale and cost-efficiency. Such a centralized repository of data allows for advanced analytics capabilities, enabling organizations to discover more and more business value in the data they generate. Since the introduction of the term “Data Lake” in 2010, the number of organizations which have adopted a data lake architecture has increased exponentially.
A Data Warehouse is an Online Analytics Processing (OLAP) system, which aims at integrating together well-defined and structured data sets (“schema on write”) in order to provide business users with the answers to a set of predefined questions and give them some (but limited) self-service reporting capabilities.
A Data Lake on the other hand is aimed at ingesting any data, thrown at it (“schema on read”), at a performant and cost-effective manner. A Data Lake stores raw data and the only data processing that is applied on the data would usually be done in order to convert the data to a more efficient format, perform data profiling and data quality checks, but it would not process the data to make it more suitable for the needs of a single downstream consumer. A Data Lake could easily be the main (or single) source of data for a Data Warehouse or could simply complement it.
The advantages of cloud services vs on-premises infrastructure and solutions are numerous. As a starter, you’ll avoid huge capital expenditures and the risk that you will under or overprovision the necessary hardware. Also, there would be no need to adjust your organizational structure just to make sure that you have teams who can manage the required on-prem hardware, software, networking, security, etc.
With AWS services you can cut the capital expenditures and replace them with much more effective and reduced operational expenditures as you pay only for what you use, your solutions can scale both vertically and horizontally depending on the workloads in a matter of minutes. Also, you can take advantage of the shared responsibility model and fully managed services, where AWS as a service provider is taking care of a great deal of the things (security, maintenance, patching, etc.), so your organization can focus on mission critical activities.