Data Lake vs Data Warehouse

What Is a Data Warehouse?

A data warehouse is a type of infrastructure that allows businesses to bring together structured data sources. Data warehouses replace the kind of structured data environment that siloed databases provided and allow for data throughout an enterprise to be accessed and utilized for analysis at once.

Structured data in data warehouses is standardized, formatted and organized. This makes it easy for search engines and other tools to understand. Examples of structured data include business customer addresses organized into columns. Credit cards, phone numbers and health records are all coded in the same way. Data warehouses are organized, making structured data easy to find. However, data warehouses aren’t wired for unstructured data.

Nowadays, data comes from a variety of sources. Data no longer represents traditional office emails, documents, audio and video files. Key data also comes in the form of unstructured data like clicks on social media, input from IoT devices and user activity on websites. All this information can be extremely valuable to commerce and business. The downside is that it is more difficult to store and keep track of than structured data. Newer solutions came about to store and manage the growing amount of this type of data, called data lakes.

Learn more about data warehouses, the benefits of moving them to the cloud and key industry use cases.

What Is a Data Lake?

Data lakes manage unstructured data. With the rise of unstructured data, solutions came about to store and manage it, such as the open-source software, Apache Hadoop, in the early 2000s. With the software, large data sets could be stored and analyzed more easily. And so began the new era of data lakes.

Unlike a data warehouse, a data lake is perfect for both structured and unstructured data. A data lake manages structured data much like databases and data warehouses can. They can also handle unstructured data that isn’t organized in a predetermined way. And data lakes in the cloud are an effective way to store diverse data and can scale up to petabytes and beyond.

Learn more about data lakes, the benefits of moving them to the cloud and key industry use cases.

Data Lake, Data Warehouse or Both?

There is an increasing reliance on both structured and unstructured information, and the latter has grown exponentially. Data warehouses can't handle different data formats and workloads.

But data warehouses are the most steadfast. They’re consistent, predictable and high performing for structured data. This means data warehouses give you a level of fidelity and confidence. To help scale, enterprises are moving on-premises data warehouses to the cloud as a more cost-effective solution.

The Benefits of a Cloud Data Warehouse

• It’s about high-quality data. Data warehouses provide structure. They make it easy to remove data that is redundant and dated. Clean, structured data enables the foundation for leveraging high-quality business intelligence and identifying key business trends. The validity of these insights correlates with the quality of your data. You’re more apt to move forward confidently with business decisions based on insights from trusted data.
• Analytics with quality data saves time. Data warehouses ensure all the sources of data being integrated are organized, cleansed and stored. This makes batch analytical processing possible on a daily basis. With good database management, you can tap into essential data analytics without slowing down data flows to your operational systems. Cloud data warehouses can offer big efficiency boosts, although if you need to address regulatory requirements, data privacy or latency issues, you may want to consider an on-premises data warehouse.
• Faster insights at scale reduce costs. As mentioned, a data warehouse provides clean and organized data. Working with clean data leads to faster insights, which enables better decision-making. When you run your data warehouse in the cloud, you can manage data at scale. This can lead to more efficiency at a lower cost.
• Long-term decision-making gets a boost. Deeper insights can happen when there is more data at your fingertips. Using a data warehouse to simultaneously store, manage and analyze in real-time leads to better long-term, data-driven decision making.

The Benefits of a Data Lake

Cloud data lakes deliver many benefits to organizations, such as:

• Quick data ingestion. Large amounts of unstructured data are a reality for nearly all industries, and data lakes provide the means to quickly store that jumble of data. • 
• Scalability. Industries that dealt in terabytes just a decade ago now verge on petabytes. Data lakes can handle colossal volumes of data — and, since data lakes live in the cloud, they can expand with the needs of your business.
• Productivity and accessibility. Good data and analytics can inform better policies, illuminate opportunities and demonstrate how resources can be efficiently used. Data lakes provide a means to rapidly store unstructured data prior to it becoming available for analytical tasks and data-driven decision-making.
• Sharper decision-making. If you don’t store data, you can’t derive insight from it. Data lakes allow decision-makers to glean insights from both structured and unstructured data.
• Better data science. Scientists and engineers need access to data. Data lakes give them more information to work with and analyze than traditional forms of data storage. AI and machine learning can benefit from data lakes, as they rely on the quality of data input into them.

To achieve business benefits from all this unstructured data, there needs to be a solid framework in place for data management. As unstructured enterprise data grows and grows, data management must be a business imperative for all.

Learn more with the CDO’s Guide to Intelligent Data Lake Management.

Why Do You Need a Cloud Data Lakehouse?

The volume of digital information stored on planet Earth has reached 64 zettabytes. This represents 60 times more bytes than there are stars in the observable universe. EarthWeb, a publication sourcing all things big data, data and statistics, and IOT, calculated that there are 2.5 quintillion bytes of data created every day in 2022. With 18 zeroes, that’s an unfathomable amount of data to store and manage, especially when you multiply that number by 365 days.²

Storing large amounts of unstructured data in one place has its challenges. If a data lake lacks standards or governance, it can quickly become a data swamp. Data swamps may be rich with information but work poorly for gaining insights. The lack of structure makes it difficult to obtain value from a data swamp. This data swamp leaves useful insights buried in its depths. Dirty data can hold a lot of information, but it’s not useful until it’s cleansed with good data management.

Enter the cloud data lakehouse, where the large amount of data in the data lake is given structure and governance. Simultaneously, the data lakehouse can still ingest unstructured, semi-structured or raw data from a variety of sources. A data lakehouse brings together the strengths of the data lake and the data warehouse on one platform. This makes the contents of a data lake more accessible to data scientists, data analysts and any other person or resource that can make use of it.

To address the volume, variety and velocity of data, companies are migrating their analytics workloads to the cloud. They are modernizing their infrastructures and applications. The combination of cloud data warehouses and data lakes offers impressive scalability and performance. They can handle that growing and evolving sea of data. Businesses can extract value from the data in real time using intelligent data management and governance tools. Businesses then have trusted data to speed up their digital transformation.

Learn more about modernizing your data warehouse and data lake in the cloud.