Data Lake vs. Data Warehouse: What Works Best for Your Business?

Key challenges of data storage

As data use grows exponentially across functions and industries, organizations are facing key challenges in managing their data which makes data management a critical business objective:

• Supersonic growth of data volumes makes it difficult to sort it and derive actionable insights.
• Dynamic global regulatory landscape, cross-border data transfers and data localization are influencing strategic investments and choices.
• Cost and complexity of data privacy is rising and data leaders are having to balance customer requests, needs, regulations and improvements in internal systems and processes.

Business leaders are now relying on best practices for collaboration, modernizing infrastructure, deploying automated solutions and increasing employee awareness to overcome these data management challenges.

This leads us to important questions: ‘Why is it crucial to choose the right data management solution for your business?’ and ‘Which factors need to be considered while choosing your best-fit storage platform?’ And here are some key pointers:

• Business requirements: Assessing exact data storage needs is the first step. Identify the types of data your company needs to manage. You also need to determine whether your primary requirements are raw, unstructured data or structured and processed data.
• Scope for growth: Data volumes will grow every second, so it is essential to opt for a scalable solution that can easily handle large amounts of data while maintaining optimal performance during peak time.
• Data complexity and VVV (Velocity, Volume and Variety): These 3Vs are actually what differentiates Big Data from old school data management. Amount of data collected (Volume), how quickly data is generated (Velocity) and the diversity of data type (Variety).
• Data security: You need to thoroughly assess the security required for your analytics and data storage before selecting an option.
• Cost and agility: Balancing budgets with implementing the most cost-rewarding option is tough. Companies often choose cloud storage rather than traditional on-premise solutions as it is more cost effective and allows you to pay as you go. Similarly, you have to weigh the cost and flexibility of data lakes and data warehouses to choose the solution that meets your budget and agility needs.

Best practices for managing data in data lake vs. data warehouses, especially for data scientists

• Conduct data profiling and quality assessments to identify anomalies, inconsistences or missing values in data and maintain data integrity.
• Implement data versioning and tracking mechanism for ease in reproducibility of analytical experiments and models.
• Define proper access controls, encryption and security techniques when conversing with sensitive information.

Data lakes: Definition and concepts

Primarily, a data lake is a massive and centralized repository of both structured and unstructured data; the purpose of this data can be undefined. Think of it as a raw and huge pool for your data.

Key features of a data lake

• Designed to handle large volumes of data
• Stores structured, semi-structured and unstructured data
• Offers lower costs as compared to traditional storage systems
• Can access and explore data from different sources
• Easy integration with various data processing frameworks
• Ensure data integrity, privacy, and compliance

Data lakes: Benefits over data warehouses

Data lakes are used to build data pipelines for analytical tools to find insights for informed business decisions. Six key advantages of data lakes are as follows:

• Data lakes are highly scalable and ideal for collecting large amounts of data. Storing data without the need to pre-define it makes it flexible too.
• Ease of configuring queries, data models and applications without any specific pre-planning. A data lake is a best-fit for real-time analytics, Big Data analytics and Machine Learning.
• Seamlessly importing data in its original format from various sources in real-time to perform real-time analysis and simulate actions in other applications.
• Smoothly managing structured and unstructured data in ERP transactions and ERP logs.
• Data in a raw state helps to make it quickly available for use and remove the need for transforming the data and developing schemas to define the answer for specific business needs.
• Lower operational costs as compared to traditional data warehouses as most tools used to manage data lakes are open source and run on low-cost hardware.

Data Lakes: Pitfalls as compared to data warehouses

Data lakes have some downsides, like:

• Complex on-premises deployment: Implementation of a data lake on-premises is more complex than its implementation on cloud infrastructure.  Though Hadoop or Splunk are available, setting up an on-premise data lake needs additional infrastructure and expertise. 
• Huge learning curve: Data scientists, experts and teams need to learn new tools and acquire data lake specific skills and there’s a steep learning curve. Businesses will have to invest heavily in training or upskilling in data lake technologies.
• Migration: Transferring data from a data warehouse to a data lake will need skillful planning, consideration and understanding of existing infrastructure and data sets. Ensuring a smooth transition and data integrity will require specialized skills.
• Query performance: Though data lakes offer fast and easy data ingestion, it is not optimized for queries in the same way as a data warehouse.

Challenges in implementing a data lake

• Reliability: Improper data sources and absence of proper tools can make it difficult for data professionals to fully rely on the data.
• Query performance: For exploratory data analysis with SQL, it is important that users receive quick responses to queries. As data lakes hold numerous files and tables, it is vital that your data lake query engine is optimized to deliver at scale.
• Governance: Data lakes traditionally have been difficult to properly secure and ensure governance requirements. Laws such as GDPR and CCPA require that companies can delete all data related to a customer if they request it.

Evaluation criteria or key considerations to get started with a data lake

• Choice of technology: Apache Hadoop and its suite has been an eternal favorite among developers of data lakes. However, in the recent past, other alternatives have arrived like Amazon, Microsoft Azure, Google data lakes etc.
• Security and user controls: Holding a treasure trove of critical business information, a data lake can be a soft target for fraudsters. How to protect your data lakes from unauthorized access will be one of the first questions that will require an answer.
• Data ingestion: Data lakes hold raw data. You will need a platform that will easily and quickly ingest structured, semi-structured, or unstructured data. It should be able to efficiently manage data streams and batch loads.
• Data processing and performance: Get a clear idea about the tools and processes offered by the platform for users. Its data exploration capabilities, scalability, and speed should match the course of your daily operations.
• Data analysis, governance and accessibility: Ensure that the data lake platform offers mechanisms to maintain data consistency, reliability and sandbox environments. It should allow you to easily incorporate machine learning and data analytics features for analysis.

After platform selection, the next step is to define your data lake implementation strategy:

• Build a project plan with goals, milestones, and action items.
• Hire experienced and expert professionals and train your internal teams.
• Define organizational roles, reporting mechanism, key performance parameters, data classification standards for storage and retrieval.
• Identify and evaluate your data sources, develop and implement data governance strategies and standards for exploration, experimentation and analysis. 

Some use cases where a data lake is more suitable than a data warehouse

• Raw data storage and exploration: Serving as a central repository for various data sources like logs, sensor data, IoT devices and social media streams, a data lake can store large volumes of raw and diverse data.

  This helps data scientists to leverage machine learning and advanced analytics for data discovery, uncover patterns and gain insights that might not be possible with structured data. Data lakes are ideally used for near real-time analysis as well as experimental analysis.

• Data warehousing offloading: Data lakes are a cost-intensive solution for transferring data from traditional data warehouses. Infrequently accessed or less structured data can be migrated from a data warehouse to data lake to reduce storage costs yet retaining access to the data. In some cases, data lakes are used as a staging area before starting implementation of data warehouses.

• Data-driven marketing and personalization: Due to storage and analysis of customer centric data like demographics, behavior patterns, purchase history, data lakes give a holistic view for personalizing marketing campaigns, improving customer segmentation and enhancing customer experience.

• Compliance and regulatory reporting: As data lakes integrate data from different sources and streams, it becomes easy for companies to ensure data accuracy, maintain data lineage and simplify generation of complex compliance reports.

Data lake architecture

As there are a number of tools available to build and manage data lakes like Azure, Amazon and Hadoop, detailed structure of your system will depend on the tool that matches your overall data integration strategy.

Data warehouse: Definition and concepts

A data warehouse stores large volumes of data from multiple sources in a single repository in a highly structured and unified manner. It can easily combine data sets from cloud, relational databases, structured and semi-structured data as long as a consistent data structure is maintained. Primarily, data warehouses are used for business intelligence, data analysis, AI and ML needs to make informed decisions.

Key features of a data warehouse

• Designed to store structured data in an optimized manner
• Involves Extract, Transform and Load (ETL) process
• Stores historical data to aid in trend analysis, historical comparisons and time-based reporting
• Supports aggregation and summarization of data
• Optimized for complex analytical queries involving multiple tables, joins and aggregations
• Ease of handling for increasing data volumes and complex queries

Data warehouse: Benefits over data lakes

More structured data builds more trust. Data warehouses establish a consistent single source of truth to present a complex picture in a harmonized way. Five key advantages include:

• Reduced data duplication: As it stores structured and integrated data from various sources, data warehouses simplify data management and reduce duplication- while ensuring consistency across the organization.
• Faster query performance: Data warehouses are highly optimized for complex analytical queries, owing to a combination of indexing, query optimization techniques and pre-aggregated data.
• Ease of use: With scalable architectures, distributed processing and parallel execution, data warehouses ensure optimal performance while dealing with increasing data volumes.
• Exploratory analysis: Data warehouses provide a platform for data discovery, data mining and hypotheses testing. Data scientists can easily navigate through integrated data to uncover hidden patterns, correlations and insights.
• Cost efficiency: Though implementation costs are higher as compared to data lakes, in the long run data warehouses reduce costs related to data management, storage and analysis.

Data warehouse: Pitfalls as compared to data lakes

While data warehouses will be a smart choice for structured data storage, it is a time-consuming process to use an ETL process. Some downsides of data warehouse include:

• Inadequate source data: Due to high initial storage costs, organizations often limit what is captured, stored and ingested in data warehouses leading to storing data for known requirements only.
• Not optimized for Big Data: Data warehouses fail while dealing with massive amount of varied data, as there are significant limitations in database queries.
• Chances of data distortion: Since data warehouses fully rely on pre-defined, pre-loaded cleansed and structured data, chances of data distortion increase.

Challenges in implementing data warehouses

• Data quality assurance: High data quality is crucial for data warehouses as data silos, manual errors, outliers and other data inefficiencies easily creep in. Ensuring data cleansing and validation practices are important to maintain high data quality.
• Complex data integration: Data warehouses contain data from diverse sources with varying formats, structures and data quality, increasing chances of data conflicts and inconsistencies.
• Data governance and compliance: Adhering to robust data governance practices involve defining data ownership, access controls and data privacy regulations. Companies must navigate through complex data governance frameworks to maintain compliance standards.
• Resource requirements: Data warehouses demand significant financial resources for hardware and software, skilled personnel, time and efforts.

Evaluation criteria or key considerations to get started with a data warehouse

• Firstly, define the required data structure and schema to handle transactional, financial and customer data. Assess whether your existing data streams align with data warehousing concepts like star or snowflake schemas.
• Define the types of queries and reporting requirements that your users have in daily operations.
• Evaluate the performance and scalability requirements for your data warehouse.
• Determine the list of existing systems and its integration capabilities with a data warehouse.
• Evaluate data governance and security measures to enforce access controls and data encryption.
• Assess the cost impact of hardware, software and licenses while also considering maintenance, administration and staffing needs.

Data warehouse architecture

Modern data warehousing has rapidly evolved from the traditional Extract, Transform and Load (ETL) process. There are three major zones in data warehouses:

• Landing zone: Data is structured as tables from transactional systems
• Curated zone: Conforms to a Data Vault, Inland or Kimble.
• Analytics zone: Data is housed in structured formats and contain dimensions such as days, weeks, months, and years.

Some use cases where a data warehouse is more suitable than a data lake

Data warehouses can be easily leveraged to offer a structured environment for data scientists to derive key insights and make informed decisions. Here are some key use cases for data warehouses:

• Business intelligence and reporting: Consolidation of data from multiple sources helps to establish a single source of truth for data scientists. Data warehouses can be used to enable in-depth analysis, identify trends, and monitor key performance indicators. With accurate and real-time reports for various stakeholders, data warehouses lead to better decision-making, improve operational efficiency and drive business growth.
• Customer analytics and personalization: With seamless integration of customer data from various touchpoints like transactions, interactions and social media behavior, data warehouses help to gain a holistic view of customers’ preferences and behaviors. It allows easy segmentation, clustering and predictive modeling for personalized marketing campaigns, targeted messaging and enhanced customer experience. Data warehouses build this crucial infrastructure to analyze huge amounts of customer data that translates in improved customer satisfaction, loyalty and revenue.
• Risk management and compliance: Companies need to actively manage risks and ensure regulatory compliance. Data warehouses are crucial for consolidating data from financial records, transaction logs and security logs and perform risk assessments and detect anomalies. With data warehouses, you can integrate external data sources including market data, industry benchmarks, risk modeling and predictive analytics to proactively mitigate risks, ensure compliance and maintain brand reputation.
• Supply chain optimization: Effective supply chain management is necessary to streamline operations, reduce costs and meet customer demands. Data warehouses help to build a centralized repository of supply chain data like inventory levels, demand patterns, market trends and supplier performance. It helps to optimize inventory levels, with demand forecasting helping vendor relationships leading to improved efficiency, reduced costs and higher customer satisfaction.
• Financial analysis and planning: Data warehouses empower users to gain insights into financial performance, profitability and budgeting. By seamlessly integrating financial data from various sources like sales, expenses and market data, data scientists can deliver detailed financial analysis, conduct variance analysis and create accurate forecasts. The ability to perform complex financial modeling and scenario analysis in data warehouses help companies to make informed financial decisions, optimize budget planning and allocating resources effectively to drive financial stability.