The Emergence of Data Lakehouse: A New Trend in Data Migration

In the world of big data, managing huge chunks of data has become a challenge for companies and business owners. Companies undergo a lot of inconveniences while analyzing data and acquiring meaningful insights from it. This mainly happens because of a lack of efficient use of technology.

Previously, most companies would use data lakehouse to store their data. However, as time progressed, a newer and more advanced data management system was introduced to mankind: Data Lakehouse. These data lakehouse accumulate the benefits of data warehouses and data lakes and present us with something more powerful and innovative.

So let us take a quick look at data lakehouse and how they could transform data management methods:

Understanding Data Lakehouse 

A data lakehouse is a data management technique that combines the best aspects of data warehouse and data lake to manage data efficiently. These data management systems are cost-effective, flexible, and can be scaled easily. One can also leverage the power of highly advanced forms of technology like machine learning, data science, and business analytics in data lakehouse to enhance data use.

The data lakehouse can be used to store data in standardized formats. You can access the data from the storage unit directly through APIs. Data lakehouse also facilitate Business Intelligence (BI) through efficient extract, transform, and load (ETL) processes. Today, most companies and organizations from all across the globe have either already implemented data lakehouse or are willing to do so soon.

In fact, in a recently conducted survey, it was seen that 66% of survey respondents have started using a data lakehouse, and the remaining are looking to do so in the upcoming years.

The Architecture of Data Lakehouse

Data lakehouse consist of five different layers:

Injection layer

The first layer is the injection layer. This layer is responsible for obtaining data from multiple sources and transferring the data to the storage unit. The data can be obtained from external sources like RDBMS, NoSQL databases, CRM applications, social media applications, etc.

Storage layer

The storage layer stores data in low-cost object stores like AWS S3. The client tools can access these objects directly using open file formats. You can store both structured and unstructured data in the storage layer without spending much on infrastructure.

Metadata layer

The metadata layer is the foundation layer of the data lakehouse. It provides metadata for all the objects in the storage layer. This layer also facilitates caching, indexing, acid transactions, data versioning, etc.

API layer

The API layer is another important layer of the data warehouse architecture. It hosts various APIs. This allows the end users to perform various tasks on the data quickly and simplistically. The API layer also provides multiple data optimization opportunities.

Data consumption layer

The data consumption layer hosts different advanced tools like Tableau, PowerBI, etc. This layer utilizes the data efficiently for various analytics tasks, including data visualization, machine learning jobs, etc.

Important Considerations to Make When Shifting from a Data Warehouse to a Data Lakehouse 

While moving from a data warehouse to a data lakehouse, the following factors require consideration:

Compute and storage decoupling

Decoupling compute and storage in a data lakehouse offers multiple advantages. By allocating computer resources depending on the specific needs of a particular workflow, you can add flexibility to your workplace infrastructure. You will no longer have to allocate much money for data storage each year.

Operating on structured, semi-structured and unstructured data

In the case of data lakehouse, you can work on structured, semi-structured, and unstructured data. This adaptability makes it easy for you to utilize the power of multiple data sources. You will easily be able to expand into new markets and offer your business the required growth.

Extensive support for multiple languages

Data lakehouse support multiple programming languages. You get access to different tools and programming languages, like SQL and Python. The centralized hub of tools and languages makes it easier for you to work on bulk data.

Optimization of data

With data lakehouse, you can optimize the data using the file partitioning method. This can improve workplace scalability and performance. You will be offered a stable platform to perform data optimization operations like removing unused files, file comprehension, etc. You can also determine the retention policy for your business data as per your needs and requirements.

Implementation of Data Lakehouse 

Here’s how you can implement data lakehouse in your workplace infrastructure:

Identifying your workplace’s needs

Before implementing a data lakehouse, you must identify your business needs. This will help you get the most out of your data management strategy, and the profitability of your business will also increase.

Choosing a reliable data lakehouse platform

You must choose a data lakehouse platform that aligns with your business goals. This will allow you to make the most of your available data. You will also face no hindrance in taking your business to the peak of success.

Implementing data governance policies and data management techniques

Next, you must implement various policies to maintain data compliance and security. You will also have to choose different data integration techniques and pipelines to acquire data from multiple sources and maintain a streamlined workflow.

Training your engineers and analysts

Once the data lakehouse system has been implemented in your workplace infrastructure, it is important to provide the necessary training to your company’s data engineers to become familiar with the new data lakehouse environment.

Benefits and Future Outlook of Data Lakehouse:

Let us have a look at some of the major benefits of using data lakehouse for managing your data:

Cost-effective data storage

Data lakehouse are a cost-effective data storage method. This will allow you to store bulk data without spending much money. Your maintenance costs will also be reduced to a significant extent.

Easy access to analyzing tools

You will be offered multiple tools to analyze the available data. You will also be able to use structured and unstructured data for analysis.

Easy data governance

As the architecture of data lakehouse is quite simple, you will face no trouble at all in governing the data. This will reduce the chances of data breaches and save you from security threats.

Enhanced capabilities

You can integrate the capabilities of data lakehouse with data analytics and machine learning to acquire real-time data insights. This helps you make well-researched business decisions.

Also, the future of data lakehouse looks quite bright, and within five years, most organizations will shift to data lakehouse from data warehouses or data lake systems. In fact, it is believed that the global data warehousing market will grow at a CAGR of 10% until 2028. Data lakehouse will also frequently use highly advanced technologies to offer users more accurate results.

Indium Software’s Cutting-Edge Solutions to Implement Data Lakehouse In Your Workplace 

Indium Software is one of the most reliable organizations for implementing data lakehouse structures in your workplace environment. Our experts use advanced data optimization techniques like Optimized Row Columnar (ORC) or Parquet file systems that allow you to maximize the available data.

Leverage our highly advanced technologies, like artificial intelligence and machine learning to create a unique place for business in this data-driven landscape.

Conclusion

Data lakehouse offers a more holistic and flexible way of managing large volumes of data. As technology continues to evolve, data lakehouse are more likely to play a pivotal role in helping you make data-driven decisions for your business and stay ahead of the curve.