What Is a Data Lakehouse?

A data lakehouse is a facility that houses structured, semi-structured, and unstructured data in its native format for an extended period. Crucially, the goal of a data lakehouse is to store data in its native format until it needs to be processed by an analytics process or application. The data will be transformed into the appropriate structure to make processing quicker and cheaper.

A data lakehouse also aims to reduce or eliminate human involvement with routine tasks through automation wherever possible. This automation includes metadata tagging, replication, aggregation, and extraction for downstream applications. The scheduled jobs usually are run on separate platforms such as Apache Oozie, Azkaban, Luigi, or Airflow.

The data lakehouse is distinct from other data platforms in that it treats storage as an inexpensive commodity. More precisely, it considers structured storage inefficient because of its high cost per unit of information. Instead, it focuses on cheaper unstructured storage, which can be aggregated to facilitate whatever processing or queries are required at the time.

The name “data lake” has become synonymous with any repository that contains data in its native format for extended periods. This is unfortunate because some organizations have started using more traditional relational databases or NoSQL implementations to store un-transformed raw data simply because they falsely identify them as a “lake.” Hence you will find people talking about Hadoop being used as a Massive Data Lake or File System being used as a Data Lake. This is incorrect. Hadoop was initially conceived of as storage for raw logs transformed at query time to accelerate analytics.

Data Lakehouse Understanding

A data lakehouse is a data solution concept that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data.

There is also the term “data lake analytics” which refers to a comprehensive system that makes it easier for businesses to unlock insights from their raw data and quickly act upon them. Data science teams then share their findings with company leaders who can use this new insight to inform business decisions and drive growth initiatives. When implemented correctly, businesses can achieve better agility and improved decision-making.

Data lake analytics is used in various ways by companies across multiple industries. One example is its role in digital marketing optimization. With its technology, marketers are able to collect information about every single customer interaction, whether it’s on their website or one of their social media pages. Further, they mine that data for patterns that could inform future ad campaigns. When appropriately implemented, data lake analytics helps companies provide a better experience for customers and generate more revenue. It’s no wonder why many high-performing organizations have already put this approach into practice.

One of the best ways to implement data lakehouse analytics is by working with a trusted, experienced professional services partner who can provide guidance and support from front-end planning to operationalization. They will create a compelling business case, define requirements, manage timelines and ensure project success throughout the entire process. In short, they handle everything, so you don’t have to.

In addition, these experts help maximize your data lake analytics investment by using proven methodologies, technologies, and industry best practices for system design, development, and implementation, ultimately providing better results in less time. What is more, they often provide systems integration services and cloud engineering capabilities for building your data lake initiatives on an open-source platform like Hadoop or within an enterprise cloud.

The key components for any data lake analytics effort include cloud-based storage, scalable processing engines, and an enterprise master data management (MDM) solution, all within a unified architecture that’s easy to use. These experts consider all of this when designing the right system for your needs, then build it accordingly. They even help you plan for rollout, organizational change management, and long-term support to ensure everyone in your organization is ready to put these best practices into practice once the system is live.

Technology at the Core of Data Lakehouse

A data lakehouse emphasizes the “lake” part of the data management paradigm. Historically, it has been challenging to keep all data in a lake for any number of reasons related to storage costs, latency, network bandwidth, etc. The data lakehouse movement is about shifting business value from proprietary systems to open source infrastructure managed by IT. The foundation of this transition is high-performance computing clusters built with commodity hardware and Apache Hadoop software stacks running next to or instead of existing EDW platforms such as Teradata Aster or IBM Netezza. These technologies enable analysts and scientists to build production use cases at scale without having access or needing responsibility for the underlying storage and compute resources that power the data management solution.

Key Features of a Data Lakehouse

The following are the key features of data lakehouses:

• A data lakehouse may be integrated with other Hadoop components, including the Hadoop Distributed File System (HDFS), Hive, Pig, Oozie, Storm, Flume, Sqoop, Spark, Zeppelin, etc.
• It supports self-declared or self-managed schemas, which reduces the overhead required in defining schemas before loading data into the lake. It also eliminates the overhead required to store and manage multiple versions of schema definition files.
• Data lakehouses provide support for metadata management. This includes both data about the contents of the lake and external metadata such as how often this dataset has been accessed, who has used it in the last six months, etc.
• It stores data organized into a “data catalog,” which is a list of available datasets and details about each dataset. For example, a data catalog may include descriptive information provided by a system administrator or collected automatically by the data lakehouse from other cloud systems using an agreed-upon interface specification.
• In many cases, data lakehouses are federated systems, which means that they may combine data from more than one source. For example, a data lakehouse might use HDFS to house user profile information files while using another cloud-based service for housing call detail records (CDRs).
• It supports application programming interfaces (APIs) to access stored data. For example, it may include an API compatible with the open-source Hadoop File System (HDFS) API so that users can submit MapReduce jobs without understanding the underlying details of how data is stored. APIs are also used by business intelligence tools and other systems that need access to external data.
• It supports managing business relationships between multiple organizations. For example, some datasets may be shared among several departments within a single organization. In contrast, other datasets may contain information related to more than one organization (e.g., customer and prospect data) or be used by all organizations that share the data lakehouse.
• A data lakehouse can be used to manage event subscriptions. Continuously, people or systems may ask data lakehouse whether new data is available on topics of interest. If new data has arrived since the last time this was checked, it is delivered to whoever requested it.

Data Lakehouse Architecture Designs

According to the new self-service data preparation workflow, the creation of modern silos is in progress. Data lakehouse architecture is built upon the following concepts:

• Simplifying the access, search, and analysis of big data with high scalability;
• Modernizing enterprise’s infrastructure for data lake operation;
• Seamlessly integrating compatibility to existing analytics environment through self-service data preparation;
• Replacing traditional storage systems with modern ones for data lakes;
• Leveraging current enterprise investments in technologies and applications.

Data lakehouses are rapidly gaining popularity, but there is immense confusion in the market about their actual definition. To make this concept clear, there is a quick description of what a data lakehouse is, how it works, and the different architectural designs available for a data lakehouse.

Data Warehouse vs. Data Lake vs. Data Lakehouse

A typical data warehouse is a system that captures structured operational data into a central repository from which business analysts can run reports and answer ad hoc queries. It loads daily transactional data into the central repository, consisting of several normalized tables on large relational database management systems (RDBMS). These normalized tables are further comprised of smaller flat files that are loaded using ETL tools. Data warehouses are often deployed in OLAP cubes for faster reporting and analysis. This is the typical deployment model of a conventional data warehouse.

A data lake is usually an append-only storage system that stores all kinds of raw data in its native format, including structured operational data as well as semi-structured and unstructured information. A data lake does not use RDBMSs or normalization but employs file systems or object stores instead. It does not have a pre-defined structure and is not governed by a metadata schema.

Finally, a data lakehouse is a system that sits on top of the data lake and provides governance, curation, search and secure access to data. It can be used as an enterprise data hub for storing raw, semi-structured, and unstructured operational data in the company’s various repositories (e.g., ERP systems and Hadoop) all in one place. This model facilitates information exchange across departments that use their own silos of information.

The goal of a data lakehouse is to orchestrate between different systems where unrefined data resides within them, including relational databases such as SQL Server, Oracle, etc., Hadoop Distributed File System (HDFS), Amazon S3 object stores or other file systems, and/or existing data warehouses. It also serves as a central search index for all the information (including the metadata). Hence, users can simultaneously view different schemata across heterogeneous repositories through this common Search interface.

The lakehouse is considered to be better than a warehouse whose structure, more commonly known as a traditional database, successfully stores massive quantities of data but often fails when it comes time to process or retrieve that information for analysis. To avoid these pitfalls, companies are turning to data lakehouses. This structure’s architecture is based on Apache Hadoop. This open-source software framework allows users to store large amounts of unstructured data in cheap commodity servers while still quickly finding the information they need. It also supports both batch processing and real-time analysis, which means you can analyze all types of data whenever necessary or convenient for your business.

Advantages of a Data Lakehouse

The advantages of data lakehouses are numerous. Firstly, a data lakehouse gains value from a single point for data entry. Secondly, all the stored data is available to all the tools and applications that need it. Thirdly, the “workbench” is a shared resource that allows users to share information and create structures in the data lake.

Data lakehouses share information with other repositories, data warehouses, or databases that can be used for ad hoc reporting. They have an open structure that allows you to change it as needed. Besides, users can import new data from different sources on an ongoing basis.

In comparison to static data warehouses, data lakehouses can be updated in real-time because they are based on a source data lake. Finally, information storage and retrieval are simplified in a data lakehouse.

Conclusion

The data lakehouse is the newest type of data center in recent years. It combines many different disciplines, including information technology, open-source software, cloud computing, and distributed storage protocols. It allows companies to store all types of data from any location in a single place, making it easier to manage and analyze.