What is a Data Lake and Does Your Organisation Need One?
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What is a Data Lake?
Digital businesses are awash with many types of internal and external data.
But where does it go?
These data sources are absolutely essential for business success, business efficiency, trends, record keeping and analysing user activity.
How can your organisation maintain secure, low-cost, flexible data infrastructure while keeping up with massive data volumes?
Businesses are pushing systems to their limits, which is why companies are flocking from traditional data warehouse management to a new system 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 centralise their structured and unstructured data, enabling them to retrieve and utilise data to boost efficiency, scalability and accelerate their growth.
Demand for Data Lakes is expected to grow globally by 27.4%.
Where did the term ‘Data Lake’ come from?
The term ‘Data Lake’ was first coined by Pentaho CTO James Dixon in October 2010. Unlike a data warehouse, it collects data in its original format. Did you know that Data Lakes are expected to grow at a CAGR of 27.4 % by 2024?
Data Lakes were originally built using on-site file systems, but these proved difficult to deploy since the only way to increase capacity was by 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 form of data architecture in the world of data science, advanced analytics, and digital-first business.
Many organisations are rapidly re-platforming their Data Lakes, abandoning old platforms and remodeling 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 Organisations Need Data Lakes?
The onset of the COVID-19 pandemic has accelerated the drive toward data reliance. Without a Data Lake, your organisation will struggle to get ahead in sales, marketing, productivity and analytics.
Power and Integration
Data Lakes allow organisations to convert raw unstructured data into standardised, structured data, from which they can apply data science, machine learning and SQL analytics with minimal latency.
As mentioned previously, 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 and 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 and 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 organisation.
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What are the Benefits of Having a Data Lake?
Unlike most databases, Data Lakes can process all types of data, including unstructured and structured data such as video and audio. These are critical for machine learning and advanced analytics. Here are some of the key benefits of using Data Lake:
- Limitless Scalability
- IoT Integration
- Flexibility
- Native Format
- Advanced Algorithms
- Machine Learning
Limitless Scalability
Data Lakes empower organisations to fulfil any requirements at a reasonable cost by adding more machines to their pool of resources – otherwise 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. Data Lakes mean you can store data whichever way you see 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.
What are The Biggest Challenges of Building a Data Lake?
While the benefits of Data Lakes are profound, organisations must be prepared for potential hiccups which may include reliability issues, engine slowdowns and security problems. These include:
- Cost
- Implementation Challenges
- Taking the Long-Term View
- Skills Shortages
- Data Outgrowing Computer Power
- Security and Data Compliance Concerns
Cost
Are you interested in building a data lake but fear the cost of implementation may be too high? Once implemented, Data Lakes can be much more cost-efficient than on-premises systems, which are difficult to deploy and expensive to maintain. Some cloud platforms are free of charge, but implementation can take a very long time and may require help from digital transformation partners.
Implementation Challenges
Data Lake implementation can be challenging even for the most experienced engineers. Whether your organisation opts for an open-source platform or a managed service, issues can include limited host infrastructure capacity, redundant data, data protection and security.
Taking the Long-Term View
It can take months to create your Data Lake and it takes time to grow your Data Lake to a stage where it becomes integrated enough with your systems to deliver real value.
Skills Shortage
As the world embraces digital transformation, more people are upskilling. Despite this, highly skilled data scientists and engineers can be very expensive due to the high demand and limited supply of these specialists.
Data is Outgrowing Computing Power
According to a 2019 study at Stanford University, AI computation has grown by more than 300,00 times since 2012 and is doubling every 3.4 months. This exponential progression results in data outpacing their host computer systems, forcing companies to invest in expanding their compute resources.
Security and Data Compliance
Organisations that want to master data security and rule governance within Data Lakes will have to invest heavily or outsource to achieve business milestones.
What is a Data Swamp?
Data can be provided in a structured, semi-structured or unstructured format. Like real lakes, Data Lakes are constantly refreshed by multiple data streams, such as emails, images and videos. Organisation is everything. A poorly organised Data Lake is referred to as a ‘Data Swamp’.
What are the four best ways to avoid a Data Swamp?
- Ensure your data is trusted, that insights are reliable and that they are immediately accessible. Data must be stored and connected. Remove any silos that separate your sources.
- Have an end-to-end strategy centred around a fully mapped understanding of desired results.
- Secure leadership alignment. This is critical for success. Without executive support for the strategy, your data insights will not be properly harnessed or utilised. According to Gartner, enterprises with a cohesive Data Lake strategy will support 30% more user cases than their competitors.
- Collect, Curate and structure your data, otherwise it will have limited value and limited monetisation potential. Too many Data Lakes become marshes of unused insights. Organisations must have a cultural and organisational alignment that strives toward data-driven insights.
What are the Best Practices for Data Lakes?
Data Lake-focused architecture, or ‘Lakehouse’ architecture, brings data science, traditional analytics and machine learning under one roof. But what are the best practices for building your Lakehouse?
Top Tips for Best Practices:
Make your Data Lake a landing zone for your preserved, unaltered data.
To remain GDPR-compliant, hide data containing personally identifiable information by pseudonymising 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.
It is vital that you 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.
How have Data Lakes Practices Improved Over the Years?
How can your Organisation’s Data Lake be Future Proof?
How have Data Lake Practices Evolved?
The earliest Data Lakes were used by data scientists using algorithm-based analytics for data mining, statistics gathering and machine learning.
But Data Lakes have since embraced multitenancy, necessitating broader-purpose set-based analytics which requires a relational database. Modern Data Lakes need to be capable of serving a wide range of functions, such as reporting at scale, self-service queries and data prep.
Many organisations are now migrating to cloud-based platforms to facilitate relational database frameworks and minimise costs.
Previous Data Lakes suffered from negligent practices including data dumping and general disregard for data compliance. However, as previously mentioned, as Data Lakes have become ingrained in recent years, data scientists have improved their practices over time.
Preparing for Tomorrow
Did you know that 90% of all data ever has been generated since 2016? In order 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 enough developers and processes, your organisation will be able to smoothly and viably manage and govern the thousands of new data sources coming your way.
Eventually, your Data Lake may need to run on other platforms. If like most organisations, your company uses a multi-cloud infrastructure, 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 kinds of data. It is good practice to store data in open file and table formats.
How can your organisation secure a successful and sustainable Data Lake deployment?
Deploy a Data-Lake Focused Design
In order to boost your organisation’s analytics and machine learning, it is vital to centralise data in one repository.
Data teams should store data in the Data Lake and acquire it directly.
Establishing the Data Lake as your organisation’s single source of truth reduces the need for time-consuming and complicated ETL and ELT workflows.
Separate Compute from Data
Traditionally, compute and data were coupled.
But thanks to advanced Data Lake cloud architecture, new open table and file formats now allow for the separation of data from the compute engines than act upon data.
Open table formats enable data engineers to simplify Data Lake environments, giving organisations flexible, scalable and cost-effective Data Lake systems.
Reduce Data Copies
Data copy build-up is a common problem in data management.
Did you know that organisations maintain an average of 13 copies of each data set?
IDC found that 60% of data storage is dedicated to managing data copies, with a collective cost of $55 billion every year.
Reducing data copies helps to keep data fresh, while reducing security and management issues associated with copy proliferation.
Why Data Lakes are the future
Future-proof Data Lake platforms are crucial to accelerating your business’ analytics and machine learning capabilities.
Data Lake analytics will empower your organisation to make informed decisions, remain competitive and stay disruptive among your business competitors.
AWS cloud-based platforms offer scalable and cost-effective objective stores alongside analytics, AI resources and data management that is ideal for a modern Data Lake.
The separation of compute and data will give your Data Lake the necessary flexibility to work with various analytic services. The open format will also provide compatibility for future tools and frameworks.
The ever-advancing, cloud-based Data Lake architecture enables businesses to efficiently centralise their organisation’s data, and with digital partners like Neo Technology, businesses can rapidly achieve a bespoke Lakehouse system that caters to their specific requirements.
No-copy architecture will increase your organisation’s productivity by eliminating the need to use data warehouses.