Enterprises can gain from a single platform that can accommodate various needs and audiences by switching to a data lakehouse without sacrificing quality or efficiency. However, this transition comes with some difficulties.
Enterprises frequently rely on data warehouses and lakes to manage massive amounts of data for diverse purposes, including business intelligence and data science. However, these architectures have drawbacks and compromises that make them unsuitable for contemporary teams. Combining the best aspects of both, a novel strategy known as a “data lakehouse” aims to solve these problems.
Data warehouses can be expensive, difficult, and rigid, requiring predefined transformations and schemas that might not be appropriate for all use cases. Data lakes often lack the quality and consistency that data warehouses offer and can be challenging to manage, unreliable, and messy.
Enterprises can gain from a single platform that can accommodate various needs and audiences by switching to a data lakehouse without sacrificing quality or efficiency. However, this transition comes with difficulties, such as ensuring compatibility, security, and governance across various data and system types.
Businesses must carefully plan and carry out their migration strategy to prevent business disruption and achieve their desired results.
Data lakehouses create a single system of insight by combining a data warehouse and a data lake. A new, open data management architecture known as a “data lakehouse” had been developed to enable Business Intelligence (BI) and Machine Learning (ML) on all data. It combines data lakes’ scale, adaptability, and cost-effectiveness with data warehouses’ data management and ACID transactions.
Organizations have historically maintained two systems as part of their data strategies: a system of records for managing their business operations and a system of insight, similar to a data warehouse, for gathering business intelligence (BI). The dual system of insight, the data lake, which provides insights from machine learning and artificial intelligence (AI/ML), emerged as big data became ubiquitous.
Also Read: Quantum Computing Trends
However, many organizations find this paradigm of relying on two different systems of insight ineffective. Extract, transform, and load (ETL) is the time-consuming process used to move data from the system of record to the data warehouse, where it is then normalized and queried for the results. Unstructured data is added to a data lake and analyzed using tools by knowledgeable data scientists.
Many companies realize how crucial lakehouses, a subset of the query accelerator product category, are in meeting a need. Usually, a data warehouse provides structured data to a data lake. The Lakehouse adds more optimization layers to increase the data’s usability for gaining insights.
Platforms for brand-new query acceleration are in the process of development. Databricks and Snowflake have introduced data clouds and lakehouses with features designed for businesses in specific industries, like retail and healthcare. Despite having a long history in decision support and business intelligence applications, semi-structured, handling unstructured data with a wide velocity, variety, and volume was neither practical nor affordable.
Despite the obvious advantages of a data lakehouse, migrating current data workloads is not easy. It could result in exorbitant costs, protracted delays, and serious disruptions to the operations that rely on the data. In essence, migration can be expensive and time-consuming, creating a material disruption for the business, potentially resulting in lost customers and revenue when data assets are already housed in legacy architecture and powering multiple business applications.
Enterprises should consider potential cost-related issues when migrating to a data lakehouse. When transferring to a data lakehouse, the following are some of the main cost-related concerns to be aware of:
Infrastructure costs: To support the storage and processing requirements of large volumes of data, the transition to a data lakehouse may necessitate investing in new hardware, software, and infrastructure. These expenses may include the cost of cloud computing, licensing fees for software and hardware, and ongoing maintenance and support fees.
Costs associated with data integration: Consolidating data from various systems to move to a data lakehouse can be time-consuming and expensive. Integration costs might include creating custom software or buying third-party tools and services to automate and streamline the integration process.
Data quality: The success of a data lakehouse migration depends on data quality. Nevertheless, ensuring high-quality data can be expensive and requires an investment in the tools and resources needed to clean and verify data and guarantee its accuracy and consistency.
Costs associated with skill sets: Moving to a data lakehouse may require specialized knowledge and skills, which may necessitate hiring new staff, providing training, or consulting outside sources. These expenses may include hiring fees, pay, and training costs.
Costs of ongoing maintenance and support: After setting up the data lakehouse, companies must consider the ongoing maintenance and support costs. These expenses may also cover system upgrades, data backups, and ongoing management.
Regulatory compliance: A data lakehouse migration project may incur high costs for regulatory compliance. Businesses must ensure that the data they manage and store in the data lakehouse complies with all applicable data protection, privacy, and security laws.
Enterprises can budget for and control the costs associated with this significant change in their data infrastructure by considering these cost-related issues when migrating to a data lakehouse.
Also Read: Why Cloud Migrations Fail How To Avoid The Common Pitfalls?
Companies should create a phased migration strategy if they have moved a sizable amount of data into a data warehouse. As a result, business disruption gets minimized, and data assets get prioritized according to analytics use cases.
A business should first create a virtualization layer over existing warehouse environments as part of this, creating virtual data products that correspond to the existing legacy warehouse schemas. It can use these products when they are prepared to maintain current solutions and guarantee business continuity.
Next, teams should order moving datasets according to cost, complexity, or current analytics use cases. To ensure gradual migration and that the new architecture satisfies the organization’s needs, they should continue to use a continuous assessment and testing approach.
After workloads have been test-moved, data architects can expand on this strategy and oversee the movement of data assets and the open formats used. Since there are numerous methods for transferring data to, from, or between clouds, this step will not be particularly challenging. In addition, all standard database migration guidelines will be followed, including application migration, security, schema migration, and quality assurance.
The decision to move to a lakehouse should not be made hastily. It should be motivated by specific business goals, not curiosity or novelty, such as improving data access and performance. Suppose a business is satisfied with its current data warehouse, and there are no compelling reasons to switch to a lakehouse.
In that case, sticking with what works and allocating resources elsewhere may be more prudent. Otherwise, it might waste resources and create apprehension among its stakeholders. Although Lakehouse may represent the future of data analytics, it is not a universally applicable solution.
Check Out The New Enterprisetalk Podcast. For more such updates follow us on Google News Enterprisetalk News.
Swapnil Mishra is a Business News Reporter with over six years of experience in journalism and mass communication. With an impressive track record in the industry, Swapnil has worked with different media outlets and has developed technical expertise in drafting content strategies, executive leadership, business strategy, industry insights, best practices, and thought leadership. Swapnil is a journalism graduate who has a keen eye for editorial detail and a strong sense of language skills. She brings her extensive knowledge of the industry to every article she writes, ensuring that her readers receive the most up-to-date and informative news possible. Swapnil's writing style is clear, concise, and engaging, making her articles accessible to readers of all levels of expertise. Her technical expertise, coupled with her eye for detail, ensures that she produces high-quality content that meets the needs of her readers. She calls herself a plant mom and wants to have her own jungle someday.
