You should understand both the advantages and disadvantages before choosing to implement a data lakehouse.

While data lakehouses have some challenges, they provide numerous benefits that outweigh these difficulties. Still, you should understand both the advantages and disadvantages before choosing to implement a data lakehouse to ensure you know exactly what your business would get.

Data Lakehouse Challenges

If you plan on implementing a data lakehouse in your organization, first consider the challenges so you can plan how to overcome them. The challenges of using a lakehouse format include the following:

• Incompatibility: One of the implementation challenges of data lakehouses is incompatibility. While a data warehouse can handle structured data, a warehouse may struggle to handle unstructured and semi-structured data like video, audio, log analytics, social media posts, streaming and text documents. Because a data lakehouse combines a data warehouse with a lake, you could still face these challenges handling these data types with a lakehouse.
• Higher maintenance costs: A data warehouse can be costly to maintain, and because of its similarities to a warehouse, a data lakehouse can also be expensive to maintain. Depending on the size of your data lakehouse, the annual cost could be significant. Typically, a data warehouse is not static, requires regular maintenance and can become outdated. Handling these needs can be expensive, and you may experience similar requirements with a data lakehouse.
• Not great for sensitive data: Similar to a data lake, a data lakehouse may not be great for sensitive data. This is due to a lack of data consistency, making it challenging to enforce data security and reliability. A data lakehouse can accommodate every data format, which can make it difficult to implement governance policies and the proper data security measures that sensitive data types require.
• Hard to build from scratch: Additionally, a data lakehouse can be hard to build from scratch. Typically, you will start with a platform built to support the open architecture of a data lake. This means you should research every platform's implementations and capabilities before purchasing.
• Relatively new, "immature" technology: Another of the architectural challenges of a data lakehouse is that it is a relatively new, immature technology. Because the lakehouse architecture is so new, it's difficult to determine whether a data lakehouse will meet expectations and deliver on its promises. A data lakehouse can be more uncertain when compared to more mature storage solutions for big data, such as a data warehouse. However, this new data storage solution may overtake more mature solutions due to the speed of modern innovation.

Once you are aware of these challenges, you can develop a plan to deal with them in your business.

Data Lakehouse Benefits

Fortunately, data lakehouses provide several advantages that often outweigh the challenges. The benefits of using a data lakehouse include the following:

• Efficiency and flexibility: Data lakehouses are highly flexible, as they can store large amounts of data in the original, raw and unformatted form. You can store data in every medium or format without needing a predefined schema. Keeping data in its native format means data lakehouses are both flexible and efficient.
• Handling different kinds of data: Another one of the benefits of a data lakehouse is that a lakehouse can handle different kinds of data, including structured, semi-structured and unstructured data.
• Cost-effective: A data lakehouse incorporates a data lake's cost-effective storage features by using low-cost storage options. Data lakehouses also eliminate the time and costs associated with maintaining several data storage systems with a single solution.
• ACID-compliant: A data lakehouse is ACID-compliant, ensuring consistency while several parties concurrently write or read data. Though only data warehouses previously supported ACID transactions, now a lakehouse can apply them to a data lake as well.
• Elimination of redundancies: A data lakehouse reduces data duplication with its single data storage platform that can meet all of your company's data needs. If you opt for a hybrid approach that includes both a data lake and a data warehouse, you could experience costly data duplication. A data lakehouse can help you avoid this data redundancy.
• Better data management: Finally, a data lakehouse can keep a large volume of diverse data and allow multiple use cases, such as reporting, machine learning (ML) and advanced analytics.

Essentially, a data lakehouse combines the benefits of both a data warehouse and data lake and eliminates many of the challenges. For example, suppose you want to remove data inconsistency or redundancy from using multiple systems or to improve data compliance and security. In that case, you may want to use a data lakehouse.

Request a demo of Dextrus, our high-performance, next-generation cloud data platform. With Dextrus, you can transform your organization's raw data into insights and information. Use Dextrus to wrangle datasets, analyze insights and maintain an accessible cloud data lake.

Ask for a Demo for Data Lakehouse Solutions

At RightData, we make your organization's data work for you. As a software product development company, we empower businesses with products that accelerate data innovation. We provide the self-service, end-to-end tools you need to accelerate value creation from your data. These tools allow you to deliver the most value and arrive at the best decisions at the lowest cost and in the quickest time.

Since 2016, we have focused on developing our flagship product, RightData tool, a data testing software. When migrating data, you need to create a set of SQL queries to validate the data before completing the migration. This vital step ensures the data is not corrupted and that the migration is successful, and having a comprehensive testing platform makes verifying your data easier.

Our Products for Data Lake Solutions

Our products include RightData and Dextrus, both of which organizations can use for data lake and lakehouse solutions.

RightData

RightData is a testing tool for data, including ETL testing, big data testing and BI report testing. This tool is flexible, scalable, efficient and intuitive. Your business can use it to identify issues related to data gaps, quality and consistency. When RightData highlights data issues in production, you can prevent credibility damages and reduce your organization's financial risk. Key features of RightData include:

• Data reconciliation
• Supported data sources
• Data validation
• Admin console
• Comparing datasets
• Reporting and collaboration
• Applications to analyze target and source datasets

Dextrus

While RightData tests the pipeline, Dextrus builds the pipeline. This includes data pipeline automation, data migration automation and data warehousing and wrangling. With Dextrus, you can transform your raw data into valuable insights and information. Key features include:

• Quick insight on datasets
• Push-down optimization
• Data preparation
• Embedded analytics
• Query-based change data capture (CDC)
• Log-based CDC
• Data validation
• Anomaly detection

This high-performance, next-generation cloud data platform can benefit your organization in several ways. Ask for a demo for RightData or Dextrus.