Unlocking the Power of Data: The Benefits and Challenges of Data Lakes
Explore how data lakes work, their advantages over other storage solutions, and the challenges organisations may face when implementing them.
Data is the driving force behind the digital economy, and to maintain a competitive advantage, organisations must manage, store, and analyse it effectively. Traditional data warehouses have limitations in handling the variety, volume, and velocity of information generated in today's digital landscape.
Luckily, there is a more flexible, scalable, and cost-efficient solution for storing and processing large and diverse datasets — data lakes.
A data lake is a centralised repository that can store any type of data in its raw format without a predefined schema. It can gather data from multiple sources and apply various analytics techniques, enabling businesses to extract valuable insights and drive innovation.
This article will explore how data lakes work, their advantages over other storage solutions, and the challenges organisations may face when implementing them.
How data lakes process information
Data lake consists of five main layers: data ingestion, data distillation, data processing, data cataloguing & metadata management, and data analytics. We will describe each of these layers in more detail below:
Data ingestion
This is the process of collecting and transferring data from various sources into the data lake. These digital repositories can ingest any type of data - structured, semi-structured, or unstructured, both in batch mode and real-time. Some examples of data sources are databases, applications, IoT devices, social media, etc.
Data ingestion tools and services, such as Apache Kafka, AWS Kinesis, Azure Event Hubs, and traditional ETL tools, are commonly used to facilitate this process.
Data distillation
The distillation layer converts the data from raw to structured, which is then stored as files or tables for further analysis.
During the conversion, the information is cleansed, denormalised, and derived, becoming uniform in terms of encoding, format, and data type.
Data processing
Data is transformed and enriched in this layer to make it ready for analysis.
Data lakes support both batch and real-time data processing, depending on the business needs and use cases. Batch processing can be done using frameworks like Apache Spark and Hadoop, which can handle large and complex data sets. Real-time processing is facilitated by tools like Apache Flink and AWS Lambda, which can provide fast and timely results.
Cataloguing & metadata management
This process involves organising and managing metadata, essentially data about data. Metadata management ensures information discoverability, lineage tracking, and quality management.
Data cataloguing tools, such as AWS Glue, Azure Data Catalog, and Google Data Catalog, help create and maintain a data catalogue, a centralised repository of metadata that provides information about data sources, data types, data formats, data schemas, data owners, data usage, data relationships, data history, and data quality.
Analytics
The analytics layer handles querying, visualising, and applying machine learning techniques to files stored in the data lake.
Amazon Athena, Google BigQuery, Azure Synapse, and similar analytics tools and services enable administrators to run SQL-based queries without requiring data movement or transformation.
Data visualisation tools, such as Tableau, Power BI, and Looker, help create interactive dashboards and reports that present insights graphically and intuitively.
Machine learning tools and services, such as AWS SageMaker, Azure Machine Learning, and Google AI Platform, enable users to train and deploy machine learning models that can perform tasks such as prediction, classification, clustering, recommendation, etc.
The business advantages of using data lakes
Data lakes enable businesses to leverage data as a strategic asset to its full potential and gain a competitive edge in the digital economy. Some of the key benefits are:
Cost efficiency
Data lakes use scalable storage solutions that are often more cost-effective than traditional data warehouses, especially for large information volumes.
This digital repository type also reduces the need for data movement and transformation, avoiding additional costs and complexity.
Flexibility and scalability
Organisations can store raw files in data lakes, allowing for flexible processing and analysis as their business needs evolve. The various analytics techniques they can employ include SQL querying, data visualisation, machine learning, etc.
As demand or volume of data grows, data lakes can easily scale storage and processing resources to accommodate the new requirements.
Improved data accessibility
A centralised repository makes data accessible to various organisational stakeholders, promoting information democratisation and collaboration. Data lakes allow users to access data using their preferred tools and platforms without requiring complex and time-consuming data integration processes.
The data cataloguing and metadata management features are also powerful tools for users to discover, understand, and trust data.
Enhanced analytics and insights
By supporting diverse data types and advanced analytics, data lakes enable deeper and more comprehensive insights, driving better decision-making and innovation.
Organisations can uncover hidden patterns and trends by analysing data across multiple dimensions and perspectives, unlocking new business opportunities or addressing potential challenges early.
Data lakes can also easily integrate machine learning and AI applications into their processes, which provide predictive and prescriptive analytics and generate more value from the same volume of information.
Quicker information processing
Data lakes can help bridge the gap between data-driven decision-making and timely responses to changes by providing real-time data ingestion and processing capabilities.
Businesses can gain near-real-time insights and take action quickly by processing data streams and micro-batches and applying real-time analytics and alerts. These streams can also come from any source. Some of the most common ones are:
IoT devices
sensors
social media
Data-driven innovation
With all data consolidated in one place and readily available for analysis, data lakes can leverage machine learning and AI to experiment with information, test new hypotheses, and generate new products, services, and solutions.
All this newly generated data leads to new potential business processes and models, effectively driving innovation and creating new business opportunities.
Besides dedicated research and analytics initiatives, data lakes also foster a data-driven culture, where data is used continuously to inform and improve business processes and outcomes.
Compliance and governance
Modern data lakes include robust data governance, security, and compliance features, helping businesses manage data responsibly, comply with regulatory requirements, and mitigate risks.
This digital repository type empowers administrators to define and enforce data policies, rules, and standards and monitor data quality and usage. It also provides encryption, authentication, authorisation, and auditing capabilities, ensuring security and privacy.
The challenges of working with data lakes
Data lakes are not without obstacles and require careful planning and execution to ensure the project's success. Some of the most common barriers faced by businesses when implementing data lakes are:
Data quality and reliability
Since data lakes store data in its raw format without applying predefined schema or validation, data quality is not always guaranteed. Some common issues are inconsistency, incompleteness, duplication, and corruption. Data quality issues can affect the accuracy and reliability of analytics results and lead to poor decisions or actions down the line.
The solution is implementing data quality checks and controls at various layers of the data lifecycle, such as data ingestion, processing, cataloguing, and consumption. These checks ensure that data is cleaned, standardised, and enriched before analysis.
Governance and security
Data lakes may contain sensitive and confidential data, such as personal information, financial records, or intellectual property. These files need protection from unauthorised access, use, or disclosure. Data governance and security challenges can come from various sources:
lack of ownership or accountability
poor data visibility
a large number of file types and formats
parallel data streams containing both sensitive and normal information
Therefore, companies need clearly defined data governance and security policies, roles, and responsibilities, as well as tools and techniques to ensure data compliance and integrity. The solutions and methodologies we recommend to our partners are:
data cataloguing
metadata management
data lineage
data masking
data encryption
data access control
data auditing
Data integration and interoperability
Data lakes can store information from various internal and external sources, such as databases, applications, files, web services, APIs, etc., with different data structures, formats, and semantics.
Data integration and interoperability problems can arise due to the difficulty and cost of integrating and harmonising data across different systems and platforms, as well as ensuring that data can be easily accessed, queried, and analysed by different users and applications.
Therefore, companies need to adopt data integration and interoperability standards and best practices, such as data ingestion frameworks, data transformation pipelines, data virtualisation, data federation, data APIs, etc., to enable seamless data connectivity and exchange.
Information discovery and usage
Data Lakes can store large volumes and varieties of data, providing valuable insights and business opportunities. However, without proper documentation, classification, and data organisation, it can be difficult for people to find, understand, and trust data. User training and tools for analytics, visualisation and machine learning are also crucial to using data to its full potential.
Businesses that want to enable data democratisation and empowerment need to invest in their data discovery and usage capabilities. Consider the following data improvement areas:
cataloguing
profiling
tagging
search
lineage
quality indicators
literacy programs
analytics tools and services
visualisation tools and services
machine learning tools and services
Transaction support
While data lakes are effective for storing large volumes of data, they lack several crucial features. No support for transactions, challenges to enforcing data quality, lack of consistency, and isolation make it nearly impossible to mix appends and reads or batch and streaming jobs. Consequently, many data lake implementation don't reach their full potential, often resulting in a loss of benefits typically provided by data warehouses.
While recent AI advancements have focused on better models for processing unstructured data (like text, images, video, and audio), data warehouses are not optimised for these functionalities.
A common strategy involves using multiple systems – a data lake, several data warehouses, and other specialised systems such as those for streaming, time-series, graph, and image databases. However, managing multiple systems increases complexity and, more critically, introduces delays as data professionals must move or copy data between different systems.
Why you need a strong tech team for data lake implementation
Given the complexity and challenges of data management, businesses often opt to work with a technical partner who can help them design, build, and manage data lakes aligned with their business goals and needs.
A technical partner can provide the following benefits:
Expertise and experience: A technical partner brings the know-how of working with various data sources, types, formats, and technologies, and helps select and implement the best solutions for the organisation's needs. Experience is also crucial in avoiding pitfalls and ensuring that the data lakes follow industry standards and best practices.
Resources and support: A dedicated tech team provides the resources and support needed to execute data lake projects, such as skilled and qualified personnel, hardware and software infrastructure, tools and platforms, security and compliance measures, etc. Also consider the support and maintenance services, such as data ingestion, processing, cataloguing, analysis, visualisation, and machine learning. Lastly, dedicated support ensures the repository is updated, optimised, and reliable.
Innovation and value: Data specialists can help businesses leverage the innovation and value potential of data lakes. They work with advanced analytics and machine learning capabilities, such as data exploration, mining, modelling, prediction, recommendation, and optimisation to get the best business results. A technical partner can also help create new business opportunities and solutions, by applying data-driven insights and intelligence to the company's products, services, processes, and strategies.
