As global data volumes are expected to more than double in size from 2022 to 2026, business leaders increasingly recognize data as a vital asset to spur innovation and strategic decisions. However, traditional data management architectures strain under the burden of surging data growth. To fully unleash data’s potential, organizations require novel solutions tailored to today’s data realities. Enter the data lake.
What is a data lake?
Defining what is a data lake involves appreciating its ability to handle your data - all of it. A data lake offers a revolutionary approach to storing and analyzing massive, varied data by providing unlimited, low-cost storage for both raw and processed information. Built for the petabyte era, data lakes allow you to dump all collected data from disparate sources into a centralized repository, including:
- Structured data – have a predefined data model and schema, like relational databases or CSV files. Examples include customer account data, product catalogs, and sales transaction records,
- Semi-structured data – do not conform to a formal structure but contains tags or markers to separate semantic elements. Examples are log files, XML documents, and JSON objects,
- Unstructured data – have no identifiable structure or schema, such as PDF docs, videos, emails, images, social media posts, or audio files.
You have the flexibility to keep your data in its original form in a data lake, eliminating the need for preliminary data structuring. This approach allows you to perform various analytical tasks, ranging from creating dashboards and visual representations to engaging in extensive big data processing, real-time analytics, and machine learning. These capabilities are instrumental in steering your business towards data-informed growth.
This "schema-on-read" approach provides flexibility to store data now and impose structure later when analyzing the data. The data lake can be queried for relevant information, and structure can be applied only to that subset of data being analyzed.
Why do you need a data lake?
Simply, you need a data lake because organizations successfully generating business value from data will outpace competitors. It is scientifically proven. An Aberdeen survey found companies with data lakes grew organic revenue 9% faster than peers. Data lake leaders applied new analytics like machine learning to novel data from:
- log files,
- clickstreams,
- IoT devices, and
- social media.
This helped identify and seize growth opportunities by better understanding and engaging customers, boosting productivity, proactively maintaining devices, and enabling data-informed decisions.
The key drivers for data lake adoption include:
- Skyrocketing data growth. Almost 90% of data ever created was generated in the last 2 years. Traditional warehouses strain under rapid, diverse data proliferation.
- Inflexible legacy systems. Siloed data in rigid legacy systems restricts analysis. A data lake breaks down barriers to data access, exposing info to new analytics.
- New data types. Unstructured data like text, audio, video, social media and IoT data holds significant untapped potential but requires flexible schema.
- Advanced analytics. Techniques like machine learning, data mining, predictive analytics, sentiment analysis and graph analytics help uncover overlooked insights but demand scalable, varied data.
- Business agility. With quick, unfettered data access, organizations can rapidly adapt to market changes, disruptors and opportunities through data-driven decision making.
- Cost efficiency. Low-cost storage and dynamic scaling improves ROI compared to costly data warehouses requiring heavy upfront investment. Pay-as-you-go pricing optimizes costs.
Key elements of data lake architecture
Answering what is a data lake requires examining its underlying architecture. While the “data lake” analogy evokes images of an informational reservoir, it involves concrete technological data lake infrastructure working in unison. Its key capabilities include:
- Scalable storage – a data lake must offer unlimited storage at low cost to consolidate enterprise data assets. Cloud object storage like Amazon Simple Storage Service (Amazon S3) provides pay-as-you-go storage for structured, semi-structured and unstructured data,
- Metadata catalog – a catalog of indexed metadata tags assigns attributes to data for organization and discovery. This enables users to efficiently query data lakes to find relevant information,
- Ingestion – data must flow into the lake from diverse internal and external sources like databases, apps, social media, mobile devices, IoT sensors, and more. Data ingestion must be continuous, automated and handle high velocities,
- Processing frameworks – open source distributed computing frameworks like Hadoop, Spark and Flink allow parallel processing of data lake information at scale for ETL, data mining and analytics,
- Analytics access – data scientists, analysts and engineers need flexible interfaces to apply preferred analytics tools and languages like SQL, R, Python, business intelligence and machine learning to uncover insights,
- Governance – data lakes require governance capabilities for managing security, access controls, data quality and metadata. This allows compliant, secure sharing of clean, trustworthy data.
Cloud infrastructure delivers key advantages required for building enterprise data lakes, including: limitless storage – way cheaper than using data warehouses, on-demand scalable processing, and turnkey analytics engines. Cloud data lakes provide higher performance, reliability and agility than on-premise deployments. Let's explore the benefits even further.
8 advantages of a data lake
A data lake architecture confers numerous advantages to harness explosive data growth for competitive gain. Understanding what is a data lake means recognizing the following benefits:
Holistic data consolidation
Data lakes allow consolidation of all enterprise data assets into a single repository for a 360-degree view. This breaks down data silos across the organization.
Think of a bank consolidating customer data from branches, call centers, web, mobile apps, and third-party credit data into a data lake. This provides a comprehensive customer view to offer personalized pricing and product recommendations.
Limitless scalability
With cloud object storage underpinning data lakes, you can store any volume of data. Cloud infrastructure delivers inexpensive, unlimited capacity along with processing power for on-demand analytics.
Schema flexibility
Data lakes employ a schema-on-read approach, only applying structure when analyzing data. This schema flexibility, unlike rigid warehouses, means you can store data now, impose schema later.
For example, a healthcare company can dump medical images, unstructured doctor notes, and structured patient records into a data lake. This allows applying schema for focused analysis on any data set.
Cost efficiency
Cloud data lakes take advantage of massive economies of scale for low-cost storage and near-infinite scalability. Why use a data lake? Because you only pay for resources used following a pay-as-you-go model, reducing wasted capacity.
Agility
Data lakes allow users across the organization to access, analyze and collaborate on data. This self-service environment, without lengthy IT delays, accelerates time to insight and adaptation to market changes.
For instance, a software company can implement a data lake to accelerate real-time insight. Product managers can now analyze customer usage data as it streams in to adapt quickly to new market demands.
Analytics freedom
The versatility supports diverse analytics. The same data can be exploited via many techniques, including:
- dashboards,
- business intelligence,
- big data processing,
- real-time analytics,
- machine learning,
- deep learning,
- and more.
Future-proofing
With endless capacity and schema flexibility, data lakes future-proof your analytics infrastructure. One of the greatest advantages of a data lake is that as new data sources emerge, and data volumes soar, data lakes can ingest limitless streams.
Operationalization
Models and analytics workflows developed in a data lake can move into production seamlessly since no data migration between systems is required. This accelerates time to value.
Main challenges of data lakes
No wonder data lakes are gaining momentum. But while promising immense value, data lakes pose daunting challenges if not architected properly. Avoid these common pitfalls when building your data lake:
- Becoming a data swamp. Without governance, data lakes risk degenerating into useless data swamps. Metadata management, data catalogs, access controls, data quality and governance are essential to ensure data reliability, security and usability.
- Security risks. With huge data volumes spanning across the enterprise, data lakes offer broad surface exposure. Comprehensive security capabilities covering authentication, access controls, encryption and cybersecurity protections are critical.
- Inconsistent performance. While enabling analytics freedom, data lakes can suffer performance issues running intensive workloads at scale unlike optimized warehouses. Performance tuning, caching, code optimization, hardware scaling and query acceleration address this.
- Siloed systems. Lacking cohesive integration with existing data platforms like data warehouses risks creating yet another disconnected silo. Well-designed data lakes complement other systems for unified analytics.
- Difficulty in adoption. Limited skills in technologies like Hadoop, Spark and data science obstruct user adoption. Providing enablement, training and support help users extract full value. Partnering with expert managed services delivers a leg up.
Data lake use cases
When well-architected, data lakes empower game changing analytics, data-driven decision making across business functions from customer engagement to supply chain optimization. Common use cases where data lakes create strategic advantage include enhanced customer experiences, accelerated product innovation, and optimized business operation.
Sales, marketing and support interactions represent touchpoints to understand customers and continuously improve customer experience. Data lakes combine customer-level data sets, i.e. data from CRM systems, marketing platforms holding buying history, social media and incident tickets. Holistic customer insights enable highly personalized, contextual engagements that boost satisfaction and loyalty.
During research and development, data lakes help test hypotheses; both these focused on CX and these focused solely at innovation. Bringing innovative offerings to market can be accelerated by refining assumptions via iterative analysis of diverse data like:
- machine performance logs,
- genomic research,
- biochemistry data sets,
- past studies,
- and IoT sensor data.
Internet of Things devices proliferate across operations, generating abundant sensor data. Data lakes efficiently store this high-velocity data. Analytics unearth optimizations across manufacturing, supply chain, logistics, oil rigs and smart facilities by uncovering idle resources, anomalies and inefficiencies. With compelling advantages from unified insights to futureproofing, data lakes are gaining traction across industries.
How to deploy data lakes in the cloud in 10 steps?
Migrating data platforms to the cloud is a priority for enterprises. Even back in 2022, 60% of corporate data was already stored in the cloud, so here is a step-by-step guide to join the growing companion of :
Step 1: define the requirements
Determine the purpose of the data lake, and identify the data sources, data types, and data quality requirements.
Step 2: Choose cloud storage service.
Select a managed cloud storage service that can scale massively and cost-efficiently to hold your raw data. This forms the foundational data lake repository. Opt for a service with high durability and availability.
For example, Amazon Simple Storage Service (S3) provides a highly durable, available and scalable data lake foundation.
Step 3: Set up the cloud environment
Create the necessary accounts, subscriptions, and resources, such as virtual networks, storage accounts, and compute instances.
Step 4: Ingest data
Implement a data ingestion framework to automatically load streaming data and bulk data into cloud storage from on-premise systems, IoT devices, databases, apps, social media etc. Choose a service that can handle diverse data formats, compress and encrypt data in motion.
Step 5: Catalog data
Catalog ingested data with a metadata management service. Automatically crawl the data to extract technical, business and operational metadata like schemas, tags and classifiers. This makes data easily discoverable.
For instance, AWS glue allows you to ingest and catalog data in a lake to make it searchable.
Step 6: Process and analyze data
Provision managed analytics engines like Spark, Hadoop and SQL services to process data and derive insights. Select services that simplify running ETL jobs, ad hoc queries, custom algorithms and machine learning at scale.
For example, Amazon Redshift allows fast analytics on petabytes of structured data, while Amazon Athena lets you run interactive SQL queries directly on data in S3 without moving it.
Step 7: Orchestrate workflows
Use a workflow orchestration service to manage and monitor data pipelines that sequence data movement, transformation, analytics and reporting workflows in a controlled, repeatable fashion.
Step 8: Apply governance
Lock down data security, access controls, auditing and compliance capabilities using cloud services for access management, key management, logging and audit. This enables governance of data lakes.
For example, Azure Security Center provides control over the security of your data lake by preventing and detecting security threats.
Step 9: Analyze and visualize insights
Build visualizations using cloud business intelligence services to uncover hidden patterns and build dashboards over curated data sets for easier consumption across the organization.
You can visualize the data in the data lake using tools and services, such as Amazon QuickSight that delivers business intelligence capabilities.
Step 10: Operationalize models
The last step is operationalizing machine learning models developed in the data lake using cloud services. Register models and deploy them for low-latency inferencing in production applications.
By leveraging these managed cloud services instead of provisioning infrastructure, you can focus on extracting value from data rather than data plumbing. Moreover, building data lakes in the cloud given results in:
- pervasive security – cloud providers incorporate extensive security capabilities and global threat intelligence monitoring,
- scalability – cloud data lakes provide practically infinite capacity,
- reliability – unmatched resilience and disaster recovery capabilities exceeding on-premise infrastructure, and
- agility – by eliminating hassles of procuring, integrating and maintaining hardware and software, cloud data lakes allow users to quickly adapt to evolving data and business needs through self-service,
- cost efficiency – Cloud providers deliver data lakes at significantly lower costs by amortizing infrastructure expenses across thousands of customers.
The data deluge contains endless potential for inquiry and innovation. A cloud data lake strategy is key to staying ahead in a data-centric world. Architect your lake with us to maximize your business’ agility, analytics versatility and actionable intelligence.
Data lake deployment with RST Software
In an era where data is more than just an asset, the importance of effective data management cannot be overstated. The exponential growth of data, spanning structured, semi-structured, and unstructured forms, necessitates a shift from traditional data warehouses to more agile, scalable, and cost-effective solutions. The data lake, particularly when deployed in the cloud, emerges as an indispensable tool in this landscape.
If you want to step into the future, elevate your business with a cloud-based data lake. This will help you unlock the full potential of your data to innovate and make strategic decisions, driving your business to new heights. If you’d like to start your journey towards data-driven excellence, contact us now and find out how we can help you unlock these unprecedented opportunities. We are an official AWS partner and look forward to applying our expertise to unlock the full potential of your business.
