Data Lakehouse Architecture: How to Transform Data Management

Data Lakehouse Architecture: How to Transform Data Management

Businesses can collect data from more sources than ever, which can lead to powerful insights and innovation. However, the variety and volume of data can also be too overwhelming, leading to underutilization and missed growth opportunities.

If you’re using a data warehouse or a data lake, you may feel limited by your current capabilities and find it hard to untangle greater complexities. However, there is an alternative – data lakehouses. We’ll cover what data lakehouses are, what makes them different from other modern architectures, and how businesses can implement them to tackle various challenges.

Data Warehouse vs. Data Lake: Key Challenges

Although data warehouse and data lake storage architectures have played a key role in data storage and analysis, each configuration has its limitations that can keep organizations from the full potential of their data.

Data Warehouse Limitations

While data warehouses can store and analyze structured, pre-defined data for businesses, the rigidity of the schema definition required can make it difficult to accommodate new data sources or evolve the warehouse with changing business needs without significant restructuring. Data warehouses also struggle with handling unstructured data, such as images, social media posts, and sensor readings.

Data Lake Drawbacks

Data lakes can store vast amounts of data in their native format, so organizations don’t have to worry about structure. However, flexibility doesn’t come without challenges, including a potential lack of organization and data quality issues. It can also be harder to support complex queries in a data lake. Plus, the sheer quantity of data can pose a security risk without appropriate governance measures and access controls.

What is a Data Lakehouse?

Instead of having to choose one or the other, a data lakehouse offers a hybrid solution for businesses that need flexibility and scalability grounded by governance and structure. Afterall, data lakehouses combine elements of data lakes and data warehouses and can support structured, semi-structured, and unstructured data.

Key Features of a Data Lakehouse Architecture

Some of the layers that make up data lakehouse architecture include:

  • Data ingestion layer: Brings data from internal and external sources into the data lakehouse
  • Data storage layer: Raw data can be saved with cloud object storage and frequently accessed data can be handled by tiered storage
  • Data processing layer: Prepares data for analysis with real-time pipelines and batch processing
  • Metastore/data catalog: Data lineage, access control policies, and schema definitions are stored here to maintain data quality and improve data discovery
  • Query engine: SQL and BI tools allow users to query and analyze structured, semi-structured, and unstructured data

A 2024 survey by Dremio found that 86% of respondents plan on unifying their data and that 70% of respondents believe half of analytics will be in data lakehouses in the next three years.

Types of Data Lakehouse Tools and Platforms

Cloud providers like Amazon Web Services (AWS) and Microsoft Azure have data lakehouse services that leverage cloud-native data processing tools and cloud infrastructure. Open-source platforms, including Delta Lake and Apache Druid, also offer core data lakehouse functionalities and can integrate with many different cloud storage solutions. Data management platforms can also have lakehouse capabilities and provide data governance, visualization, and integration capabilities. 

Benefits of Adopting a Data Lakehouse Architecture

Moving your data over to a new architecture can feel difficult, but adopting a data lakehouse architecture comes with many benefits that outweigh the cost of switching.

Improved Decision-Making

By providing a unified view of your data, lakehouses eliminate silos and centralize both your structured and unstructured data in one platform. When all data is available in the same place, businesses can conduct holistic analyses and make better data-driven decisions.

Because data lakehouses support more data formats, the configuration also allows businesses to leverage more powerful analytics tools. This can help organizations identify previously hidden patterns and predict trends with greater accuracy.

Better Performance and Scalability

When data volume and processing needs change, data lakehouses can scale to meet new demands. This improves performance and cuts down on manual provisioning. Since real-time processing is easier with data lakehouses, businesses can gain access to valuable insights much faster, giving them a competitive edge.

Simplified Data Management and Governance

Instead of being relegated to one data type, data lakehouses enforce governance policies across all data types, improving the consistency of data quality and ensuring regulatory compliance. When all types of data are stored together, the central repository makes data management more straightforward, improving the user’s ability to discover and understand relevant datasets they need to review or analyze.

Cost-Effectiveness and Efficiency

Cloud object storage is a cost-efficient way to store data in a lakehouse, meaning expenses are lower compared to more traditional solutions. Data lakehouses also cut down on the need to manage multiple disparate systems, reducing operating costs and increasing efficiency.

Use Cases and Applications of a Data Lakehouse Architecture

The versatility of data lakehouses makes them ideal for several use cases and analytical needs. Here are a few applications that may make a data lakehouse attractive to your business.

Advanced Analytics and Business Intelligence

While traditional architectures can result in siloed data, data lakehouses can create a 360-degree view of user data. This can make recommendations and user profiles more relevant, and can also help businesses identify trends to develop new products and services.

Advanced analytics and business intelligence can also enable organizations to analyze both historical and real-time information, making it easier to pinpoint patterns that may indicate fraudulent activity.

Machine Learning and Artificial Intelligence

Machine learning and artificial intelligence can predict potential equipment failures for manufacturers, provide personalized recommendations to retail shoppers, and analyze call records to find customers at risk of churning. Because data lakehouses aren’t limited in their ability to store and analyze data, machine learning, and artificial intelligence can use several different data sources for more nuanced data-driven decisions. The Dremio survey found that 81% of respondents are using data lakehouses to support AI applications and models.

Real-Time Data Processing and Streaming Analytics

Data lakehouses can ingest and process data streams from connective devices in real-time. This is important in situations where real-time decision-making is a must – for example, heath sensors on patients or sensor data from smart grids. Real-time data can also improve response time during major sales or business events, getting a handle on customer sentiment more efficiently.

What Industries Benefit the Most from Using a Data Lakehouse Architecture?

Any businesses or industries that deal with a complex array of data can potentially benefit from data lakehouse architecture.

  • Financial services: Everything from customer transactions, sensitive personal data, and social media sentiment can be gathered by banks, investment firms, and insurance companies. Data lakehouses can help businesses in this industry detect fraud, mitigate risks, and personalize products for users.
  • Retail and eCommerce: When customers make purchases, browse websites, and sign up for loyalty programs, the data can be aggregated into a lakehouse for a more unified view of individual behavior.
  • Manufacturing: Manufacturers are leveraging Internet of Things (IoT) devices more in the production pipeline for real-time reporting on performance and operation. Data lakehouses can help with predictive maintenance, as well as optimization of certain production processes.
  • Healthcare: Healthcare organizations collect sensitive health data, including patient intake forms, historical records, and imaging results. Data lakehouses can form connections between patient data and other sources of information, streamlining and personalizing treatment plans and other patient experiences.
  • Government: Government agencies can be altered to emerging threats, optimize their resource allocation, and aggregate smart city sensor data in a data lakehouse.

How to Evaluate if a Data Lakehouse Architecture is Right for Your Business

For some businesses, traditional data architecture will be enough. However, if you’re struggling with data volume, variety, or management, or you’re not getting enough out of analytics, you may want to make the switch to a data lakehouse.

Data Volume and Variety

If your organization amasses a large volume of data, either structured or unstructured, handling the scale with a data lakehouse can be worth the investment. You’ll also want to think about the variety of your data. If you have some structured databases, some sensor data, and information you want to collect from social media feeds, data

lakehouses can help you manage and store a variety of formats, giving you a unified platform for your data.

Analytics Requirement

Traditional architectures can accomplish simple reporting, but if you’re looking for more advanced analysis using AI or machine learning or looking to combine data from different formats into one reporting platform, data lakehouses can help you form deeper analyses and reach more nuanced insights.

Current Data Management Challenges

Think about your current data management struggles. If data silos, limited storage options for unstructured data, or data governance issues exist due to your present data architecture, lakehouses can help.

How to Design and Implement a Data Lakehouse Architecture

The more careful you are in planning your data lakehouse architecture, the more success you’ll have in implementation. Here are the steps businesses should follow when designing their ideal data lakehouse setup.

Outline Your Business Needs and Goals

Your business needs and goals will shape what your data lakehouse looks like and what services you choose to support it. Start by analyzing the different types of data you need to store and access and their level of structure. What current data sources are you storing, and what might you want to add once you incorporate a data lakehouse?

A data lakehouse should work for your business and the specific problems you want to solve. By identifying your use cases early, you can start formulating your data ingestion strategy, governance policies, and list of potential tools.

Knowing what success will look like for you can also help you track how the data lakehouse has impacted your business. Do you want to speed up your decision-making, improve your ability to conduct data-driven marketing campaigns, or optimize your resource allocation? Establish the metrics you will use to track success early.

Research and Select Your Cloud Platform and Services

Decide whether you want to work with a major cloud provider, such as AWS or Azure, or a third-party provider for data lakehouse services. Your ultimate decision will come down to a combination of features, integration possibilities, pricing, scalability, and tools that each platform carries and supports. Your cloud platform should work with whatever services you choose, whether they are open-source or commercial

Define Your Data Ingestion Strategy

How will data be extracted from databases, social media platforms, applications, IoT devices, and any other sources you may want to pull into your data lakehouse? What should be streamed in real-time and what can be batch-processed?

Once you know how you want data to come in, you’ll also want to establish a process for transforming, cleaning, and validating data before it goes into the data lakehouse. This can improve consistency and data quality.

Outline Your Data Architecture and Governance Principles

Even though data lakehouses can store structured, unstructured, and semi-structured data, you will want to outline guidelines for data schema and structure based on how you want to use them. To maintain data usage and regulatory compliance, create policies for access control, data security, and data retention.

Establish Your Security and Access Controls

Protect your repository of structured and unstructured data through access controls, intrusion detection systems, and encryption, keeping bad actors out via multiple tactics. Not all users will need access to all data held in the data lakehouse. Assign read, write, and modify permissions based on user roles and responsibilities held at the business to bolster security.

Develop a Plan for Monitoring, Optimization, and Management

Address new issues quickly by implementing monitoring that checks for data quality and system performance. Explore cost-optimization strategies based on data storage usage and designate a team to conduct ongoing data lakehouse management. This can include security updates, performance optimization, and user support.

Need Help Managing Your Data?

Data lakehouses offer a powerful solution for organizations struggling with data volume, variety, or management limitations in the cloud. But determining the best-fit cloud environment to support data lakehouse architecture requires careful planning, expertise, and the right cloud partner. At TierPoint, our team of cloud experts can help guide you in the right direction – contact us today to learn more. In the meantime, download our whitepaper to explore different cloud options available for data management.

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