How can Data Lakes Maximise Analytics and Machine Learning?
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Is Your Organisation Struggling to Keep Up With Massive Data Volumes?
Digital-first Businesses are awash with many types of internal and external data. These sources are essential for boosting business efficiency, record keeping and analysing user activity and trends.
But where does it all go? With data pushing businesses to their limits, how can they maintain secure, low-cost, flexible data infrastructure whilst accumulating exponential masses of data?
Many companies are migrating from traditional data warehouse management systems to a new medium known as the ‘Data Lake’.
A Data Lake is a consolidated, centralised repository that houses various forms of data in their native format from disparate applications within a company. It allows data scientists to locate and analyse large quantities of data quickly and accurately.
Businesses that use Data Lakes can safely store, retrieve and utilise their structured and unstructured data to accelerate growth, boost efficiency and scale.
As of last year, global demand for Data Lakes is predicted to grow by 27.4%.
What are the Origins of Data Lakes?
The term ‘Data Lake’ was first coined by Pentaho CTO James Dixon in October 2010. They were originally built using on-site file systems, but these proved difficult to deploy since the only way to increase capacity was adding physical servers.
This made it difficult for organisations to upgrade their systems and increase capacity.
However, since the early 2010s, the rise of Cloud-based services has enabled companies to build and manage Data Lakes without having to build costly on-premises infrastructures.
Data Lakes are now a trusted and established for of ata. Architecture in the world of data science, advanced analystics, and digitial-first business.
Many organisations are rapidly re-platforming their Data Lakes, abandoned legacy platforms and remodelling data.
If you think of a Data Mart as a store of bottled water, cleansed and packaged and structured for easy consumption, the Data Lake is a large body of water in a more natural state. The contents of the Data Lake stream in from a source to fill the lake, and various users of the lake can come to examine, dive in, or take samples.
Why do Digital Businesses Need Data Lakes?
The onset of the COVID-19 pandemic has accelerated the drive towards data reliance. Without a Data Lake, organisations will struggle to get ahead in sales, marketing, productivity and analytics.
Power & Integration
Data Lakes allow organisations to convert raw unstructutred data into standardised, structured data, from which they can apply data science, machine learning and SQL analytics with minimal latency.
As aforementioned, Data Lakes can seamlessly integrate a broad range of data formats and sources including binary files, images, video and audio.
Any new data that arrives at the lake will be up to date.
Centralisation & Democratisation
Centralisation ensures data security and eliminates the risk of duplication and collaboration problems.
With a centralised Data Lake, downstream users will know exactly where to look for all required sources, saving them time and boosting efficiency.
The flexibility of Data Lakes enables users from a wide variety of skills backgrounds to perform different analytics tasks in unison.
Sustainability & Affordability
Data Lakes are sustainable and affordable because of their ability to scale and leverage object storage.
Furthermore, in-depth analytics and machine learning on flexible data are currently among the highest priorities for organisations.
The prediction capability that comes from flexible Data Lakes can drastically reduce costs for your organisations.
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What are the Key Benefits of Data Lakes?
- Limitless Scalability
Data Lakes empower organisations to fulfil any requirements at a reasonable cost by adding more machines to their pool of resources. This process is known as ‘scaling out’. - IoT integration
Internet of Things (IoT) is one of the key drivers of data volume. IoT device logs can be collected and analysed easily. - Flexibility
Did you know that 90% of all business data comes in unstructured formats? Data Lakes are typically more flexible repositories than structured data warehouses, meaning companies can store data in whichever way they sit fit. - Native Format
Raw data such as log files, streaming audio and social media content collected from various sources is stored in its native format, providing users with profitable insights. - Advanced Algorithms
Data Lakes allow organisations to harness complex queries and in-depth algorithms to identify relevant objects and trends. - Machine Learning
Data Lakes enable integration with machine learning due to their ability to store large and diverse amounts of data.
Data Lake Best Practices
Lakehouse architecture brings data science, traditional analytics and Machine Learning under one roof. What are the best practices for building your Data Lake?
Top tips for building your Lake House:
- Make your Data Lake a landing zone for your preserved, unaltered data.
- To remain GDPR-compliant, hide data containing personally identifiable information by psuedonymising it.
- Secure your Data Lake with view-based ACLs (access control levels). This will ensure better data security.
- Catalogue the data in your Data Lake to enable service analytics.
To avoid a data swamp, your organisation must have a clear idea of what information you are trying to accumulate, and how you want to use it.
With a clear strategy in place, your organisation will upscale successfully and meet the demands of stakeholders.
You must move with the times by incorporating modern Data Lake designs that can viably meet the demands of today’s data-driven culture.
Organisations that use AI and up-to-date data integration will be able to analyse data with greater accuracy.
Integrating DevOps and ensuring clear regulations to prevent data wildness will guarantee data compliance and keep your Data Lake clean.
Are you Ready for Tomorrow
Did you know that 90% of all data ever has been generated since 2016? To maximise your Data Lake value in the long term, you must make sure that it has enough capacity for future projects.
This will mean expanding your data team. With Agile developers and DevOps processes, your organisation will be able to run a smooth and viable operations that manages the thousands of new data sources that come your way.
Eventually, your Data Lake may need to run on other platforms. If like most organisations, your company uses a multi-Cloud infrastructure, then your Data Lake will need a future-proof, flexible and Agile infrastructure.
Using data vault methodology is the best way to ensure the continuous and steady onboarding of new data. It is good practice to store data in open file and table formats.
To conclude, there are many different methods that Agile organisations can implement to increase their Sprint Velocity.
Done correctly, the combination of Sprint Velocity and high-quality software technology has the potential to help your organisation double its efficiency and productivity.
It will also help you avoid overpromising your clients and stakeholders on product and service delivery.
The ability to move quickly is paramount but going that extra mile to plant the seeds of efficiency is key to ensuring productivity and sustainability in the long term.