Data Lake vs. Data Warehouse: How Data Integration Bridges the Gap

From personalization to predictive maintenance, targeted marketing or stopping your company becoming a target of cyberattacks, data is gold in today’s business world.

But how do you collate the data you need?

In this article, we’ll be looking at data warehouses and data lakes for storing high volumes of data, and how SEEBURGER helps you get your data where and when you need it.

What is the difference between a data warehouse and a data lake?

As data continues to grow in volume, variety, and velocity, organizations often have to make a decision: Should they implement a data lake, a data warehouse, or both?

While both storage solutions have their strengths, the choice isn’t always straightforward.

Before considering which solution might be best for your business needs, it’s important to understand the fundamental differences between data lakes and data warehouses.

What is a data lake?

Think of a data lake as a vast, unfiltered reservoir of data. It stores raw, unstructured, and semi-structured data from various sources without imposing a predefined schema. This flexibility allows organizations to ingest data as-is, making data lakes ideal for data scientists and analysts who need access to a broad range of data types for exploratory analysis, machine learning, and big data analytics.

Figure 1: Examples of unstructured data

What is a data warehouse?

In contrast, a data warehouse is more like a curated library. It stores structured data that has already been processed and organized into a consistent format according to a predefined schema so that it is ‘ready to go’ for analysis. Data warehouses are optimized for running complex queries and are widely used for business intelligence (BI) and reporting. They enforce data quality, governance, and performance, which is essential for organizations that need reliable, high-quality data for decision-making.

The data in a data warehouse can be current or historical and can come from a variety of sources, including operational systems such as for product information management (PIM), warehouse management, customer relationship management (CRM), or an enterprise resource planning system (ERP). The data is integrated into the data warehouse through an operational data store, through the onboard integration capabilities of your ERP, or through a dedicated integration platform such as the SEEBURGER BIS Platform. APIs are most commonly used, although there are various other connectivity options, including MFT.

A company may also maintain smaller, department or function-specific data marts, either as subsections of a data warehouse or as standalone systems.

The role of data integration platforms in collating data

A crucial element that often gets overlooked is how data integration platforms enable smooth data flow into both data lakes and data warehouses. These platforms connect disparate data sources, transform data as needed, and ensure it is accessible and ready for analysis, enhancing the capabilities of both storage solutions.

Which is better, a data lake or a data warehouse?

In the chart below, you can see some of the key differences between a data lake and a data warehouse:

It’s easy to fall into the trap of believing that one solution is superior to the other. Some may assume that data lakes are always better due to their flexibility, while others might think data warehouses are too rigid for modern needs. However, the reality is that each serves distinct purposes, and the choice depends on your specific requirements.

Let’s unpack some of these myths and understand why each serves unique purposes:

Myth 1: Data lakes are always better because of their flexibility

Data lakes are celebrated for their flexibility and ability to store vast amounts of raw, unstructured, and semi-structured data without imposing a rigid schema. This attribute makes them incredibly attractive for organizations that want to collect and store data without worrying about immediate structuring or processing. The freedom to ingest data in its raw form allows for advanced analytics, data science, and machine learning experiments, where diverse data types can be utilized creatively.

However, this flexibility is a double-edged sword. Without proper management, data lakes can turn into “data swamps,” where data quality deteriorates, making it difficult to extract meaningful insights. The lack of enforced structure and governance can lead to challenges in data retrieval, consistency, and overall usability, particularly for business users who need clean, reliable data for reporting.

Myth 2: Data warehouses are too rigid and outdated for modern needs

On the flip side, data warehouses are often seen as too rigid or inflexible, particularly when compared to the perceived freedom of data lakes. Data warehouses require data to be processed and structured before it is stored, which some may view as a drawback in today’s fast-paced, data-rich environments where the ability to quickly adapt to new data types and sources is valued.

However, it is precisely this structured approach that makes data warehouses indispensable for many business intelligence and reporting requirements. They provide the high levels of data quality, consistency and performance that are essential for reliable decision making. The schema-on-write approach of data warehouses – otherwise known as extract, transform, load (ETL) – ensures that data is cleansed, organized and optimized for complex queries using SQL, which is essential for producing accurate and timely insights to drive strategic business actions.

Reality: Each serves distinct purposes based on specific requirements

The key to understanding the value of data lakes and data warehouses lies in recognizing that they are not mutually exclusive; rather, they are complementary tools designed to address different aspects of data management:

Data lakes excel in scenarios where agility, scale, and the need to handle diverse data types are paramount. They are ideal for businesses engaged in exploratory analytics, machine learning, and data science, where the goal is to harness raw data for insights that drive innovation.

Data warehouses shine in environments where structured data, data quality, and performance are critical. They support complex analytical queries and are optimized for business intelligence, making them ideal for reporting, dashboarding, and trend analysis that require high data accuracy and reliability.

Bridging the gap between a data lake and a data warehouse with an integration platform

Data integration platforms are pivotal in bridging the gap between these two solutions. They provide the means to connect, transform, and manage data across both lakes and warehouses, ensuring that data is accessible, high-quality, and ready for whatever purpose it serves—be it raw analysis from a lake or structured reporting from a warehouse.

By integrating data from various sources and maintaining consistency across both environments, data integration platforms help organizations avoid the pitfalls of choosing one over the other. Instead, they enable a holistic data strategy that leverages the best of both worlds, tailored to meet the specific needs of different use cases within the organization.

How does an integration platform extract data from source systems for storage in a data warehouse or lake?

The SEEBURGER BIS platform comes equipped with a variety of adapters, connectors and data transfer capabilities to read data from source systems. It can retrieve the data through a variety of push, poll and pull methods.

In a push method of data retrieval, the data producer, whether this be a transaction system, such as an e-commerce or POS system, a data feed, a smart device or something else, notifies the SEEBURGER BIS Platform that new data is available. The producer then connects to the SEEBURGER BIS to transfer the data. There are a number of technologies on the BIS which could be used for this, including a database listener, a REST controller, an RFC server, an SFTP server and a web service controller.

In a poll method of data retrieval, the SEEBURGER BIS Platform connects to the producing system and pulls data at defined intervals. In a pull method of data retrieval, the SEEBURGER BIS Platform connects to the producing system on an ad-hoc basis. There are a number of technologies on the BIS which could be used for this, including a database client, LDAP, REST and RFC clients, and a huge range of pre-programmed connectors to popular SaaS software and services.

Alongside its EAI/A2A capabilities, the SEEBURGER BIS platform has API integration and management capabilities, as well as MFT for fast, secure transfer of high data volumes. It also supports data streaming with Kafka.

How do you analyze the data within a data warehouse?

Data analysis can take place inside the data warehouse, or the data can be moved out of the data warehouse to a specialist environment or visualization tool for deeper, highly customized analysis.

Data needs to be transformed into a schematic format before being stored and analyzed. Common formats include CSV, TSV, Parquet and ORC as these can be queried using SQL. Data warehouses are increasingly capable of handling semi-structured data such as JSON, XML and Arvo. The SEEBURGER BIS Platform has an any-to-any converter to transform data into the required format for your data warehouse and business analytics.

How do you write data to a data lake using SEEBURGER BIS?

A good solution for writing data to a data lake would be to use the SEEBURGER BIS HDFS Adapter (Hadoop Distributed File System) or a prebuilt connector. Data can be ingested into on-premise and cloud-based data lakes such as Azure or AWS. The adapter can transfer data packages to the data lake in one job, or split larger jobs into smaller, parallel steps and merge the data packages later in the data lake.

Although data in data lakes don’t need a standardized format, if you wished to harmonize your data at this stage, you could use the AI-assisted BIS mapping designer to automatically convert certain files into a format such as Arvo or Parquet.

When is a data lake better than a data warehouse?

When deciding between a data lake and a data warehouse, consider your organization’s specific needs:

Choose a data lake if you need to handle large volumes of raw, unstructured data, such as logs, social media feeds, I(I)oT sensor data, or streaming data. Data lakes are ideal for use cases involving advanced analytics, machine learning, and exploratory data analysis, where flexibility and scale are key.

When is a data warehouse better than a data lake?

Choose a data warehouse if you require structured, high-quality data for BI, reporting, and decision-making. If your organization relies heavily on historical data analysis, trend reporting, or compliance with stringent data governance standards, a data warehouse provides the necessary structure and reliability.

Regardless of your choice, data integration platforms can streamline the data preparation and ingestion process for both data lakes and data warehouses. They enable businesses to leverage the strengths of each solution by integrating data from various sources, transforming it as needed, and ensuring that it’s accessible and ready for analysis. An integration platform like the SEEBURGER BIS ensures that whether data is destined for a lake or a warehouse, it arrives in the right format, at the right time, and with the right quality.

Hybrid approaches and future trends in collating data

As data needs evolve, many organizations are adopting hybrid approaches, using both data lakes and data warehouses in a complementary fashion. This trend is fueled by the rise of data lakehouses – solutions that combine the scalability of data lakes with the performance and management features of data warehouses.

In hybrid environments, data integration platforms are indispensable. They orchestrate data flows, maintain data quality, and support real-time data needs, ensuring that all data, whether in a lake or warehouse, is harmonized and accessible. This integration is crucial for maximizing the value of your data strategy and keeping your analytics ecosystem agile and efficient.

Data integration platforms can greatly simplify the implementation of both data lakes and data warehouses. They reduce time to value, improve data governance, and provide a unified interface for managing data flows across your entire data ecosystem.

Select a platform that aligns with your business needs, with features like scalability, ease of use, and compatibility with your existing data infrastructure. The right platform can make the difference between a successful implementation and a complex, costly endeavor.

Why SEEBURGER

The SEEBURGER BIS Platform has a number of EAI/A2A and data transfer capabilities which simplify integration to a data warehouse, a data lake, or a mixed environment. It also supports stream processing.

It is available as an iPaaS on the public cloud of your choice. Reduce data transfer costs and latency when using the cloud-based data lake or data warehouse capabilities of a hyperscaler such as AWS by having your integration platform within the same cloud environment. The SEEBURGER BIS Platform can also be deployed from the SEEBURGER Cloud as well as an in on-premises installation.

The real value of the SEEBURGER BIS Platform is that you can use it for so much more than moving data to and from data warehouses and data lakes. Once you have invested in the system, use the automation capabilities to streamline all your business processes, take advantage of the EDI capabilities for automatic, electronic communication with your suppliers and customers or add e-invoicing capabilities for digital invoicing with a huge range of countries.

Source: https://blog.seeburger.com/data-lake-vs-data-warehouse-how-data-integration-bridges-the-gap/