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Cloud & Automation: Changing CSPs’ OpEx outlook
An exponential increase in data generation has contributed to the evolution of data management from transactional systems to enterprise data warehouses and data lakes. As the volume of unstructured data grows—80 percent of all new data is unstructured—traditional data warehouses alone cannot manage all the business requirements. Instead, data-driven businesses need a comprehensive ecosystem that also includes data lakes that allow the storing of raw data in its native format, adding flexibility and scalability to enterprise data management.
However, data lakes must be integrated with data warehouses to maximize data potential. Today, 67% of business leaders consider data integration critical for business intelligence and analytics. Integration is a key driver of data-driven business models, enabling organizations to improve data usability and help achieve EBITDA growth of 7 to 15 percent.
The first generation of data storage architecture collected data from operational databases and stored it in a central warehouse. The data was written with schema-on-write, which ensured the data model was optimized for downstream business intelligence and used for decision-support systems.
Data volume exploded with the Internet, and the first-generation data architecture faced challenges. It became costly as enterprises had to provision and pay for the peak user load and data under management. A significant part of new datasets included unstructured data that data warehouses could not store and query. The limitations led to the second generation of data storage architecture.
The second generation of data storage architecture, characterized by data lakes, offered greater flexibility in data storage with a schema-on-read approach. It allowed enterprises to store raw data, lowering the cost, but it created problems of data quality and governance downstream.
The third generation of data storage architecture emerged in 2015 when advanced data lakes started replacing the Hadoop File System (HDFS) used in the previous generation. The latest architecture has superior durability and geo-replication and is low-cost, with the provision for automatic, even cheaper, archival storage. The two-tiered data lake and warehouse architecture is preferred in the industry and predominantly used by Fortune 500 enterprises.
Data lakes allow organizations to store diverse data types from multiple sources in their original form. By contrast, data warehouses are structured environments utilizing a schema-on-write approach; necessitating data sets to be processed and transformed before storage. The two data storage systems are integral components of a data-driven architecture, and integrating the two helps organizations optimally utilize their data resources. This new architecture is increasingly referred to as a Lakehouse, a blend of data lake and data warehouse.
Reasons to adopt the third-generation data storage architecture:
At the first level, the data lake serves as a low-cost scalable pure data storage, allowing a company to store data indefinitely before using it for analytical purposes. It offers organizations scalability to handle large volumes of all data types – structured, semi-structured, and unstructured.
The structured environment of data warehouses is optimized for business intelligence activities, ensuring high performance for complex queries and reporting.
The integration enables organizations to scale their data storage and processing capabilities according to fluctuating data volumes without impacting performance.
The low cost of data lakes allows companies to store inactive or rarely used data, which an organization can use to generate insights without exceeding storage limitations or increasing the size of the data warehouses.
Additionally, the organization can optimize costs by keeping high-intensity extraction of relational data in enterprise data warehouses while migrating lower-intensity extraction and transformation tasks to a data lake. Data lakes often rely on open-source technologies and can be hosted on the cloud, offering an additional cost advantage.
A hybrid system leveraging optimized data warehouses for high-performance queries and data lakes for generic requirements and data storage provides organizations with a cost-effective solution.
Data scientists can leverage data lakes to build prototypes for analytical programs before processing and aggregating data in the warehouse for implementation.
Netflix[UB1] has implemented a data lake called Keystone, hosted on S3 and organized as Apache Iceberg tables. Netflix uses Apache Spark for ETL and other compute-intensive tasks and Python for last-mile data processing. The company has developed a Fast Data library for Metaflow to enable fast, scalable, and robust access to the data warehouse. The data lake stores data such as user behavior, device logs, and content viewing history. Integrating the data lake and warehouse enables it to personalize content recommendations and improve the overall user experience.
When data lakes are integrated with data warehouses, the raw data stored in the lakes is subjected to data governance and quality standards before channelling to the warehouses. Data cleaning and transformation enhance data quality and consistency, ensuring the reliability of analytics and insights derived from data.
The integration of data lakes and warehouses is an integral element of organizational data democratization initiatives. It enables organizations to create self-service reporting tools that allow employees to access audited data for customized reports.
The ability of a data lake to handle compute-intensive tasks and its distributed nature enable organizations to conduct advanced analytics or deploy machine learning programs. The integration of data lakes and warehouses enables organizations to build efficient data-intensive applications seamlessly, combining insights gained from data lake resources with a data warehouse. For example, organizations can perform real-time data processing in data lakes and historical trend analysis in data warehouses and collectively use the insights for better decision-making. Coca-Cola Andina built a data lake that helped the company increase data-driven decision-making and enhance analytics productivity by 80%
The integration of data lakes and data warehouses is a complex exercise that requires specialized skills.
Integrating data lakes with data warehouses involves combining data that are different in formats, structure, and scale, which can be complex and prone to errors. It requires developing efficient data pipelines and workflows to transform and load data while minimizing errors.
It needs to tackle the issue of siloed data sources in growing enterprises. Data acquisition and ingestion strategies need to cover all and every data source, frequency of ingestion, and the volumes and variety of data.
The integration can significantly impact performance due to increased data loads. If the quality of data is not maintained, data lakes can become data swamps, making it challenging to run queries and extract insights from the integrated data.
Even though data warehouses have robust data governance and management practices, the flexibility and scalability of data lakes can lead to governance and compliance challenges. As the data in data lakes constantly changes, there is a threat of data vulnerability and security breaches.
In addition to resources skilled in data science, engineering, and cloud computing, it requires professionals with expertise in governance and compliance, security, and risk management. Many organizations may not have the requisite expertise and resources in-house.
Integrating semi-structured and unstructured data from data lakes into data warehouses can lead to inconsistencies and inaccuracies. A strong data governance framework that clearly defines data ownership and data standards and ensures compliance with relevant regulations, such as GDPR, is essential to maintain data consistency and quality.
Historically, data integration involved moving static relational data between warehouses. However, with data lakes storing real-time data, organizations must leverage modern integration technologies to handle live, operational data in real-time. Organizations must use streaming and event-based data integration for live data transfers and Artificial Intelligence to enhance security and compliance.
Cloud-based integration solutions help ensure data accuracy in high-volume data transfers between data lakes and warehouses, implement standardization and automation, validate data at scale, and enforce a tracking plan to ensure data integrity. Hyperscalers offer cloud native services for storing data in lakes and warehouses, optimizing storage costs for customers as well as keeping storage separate from compute. This approach helps improve overall efficiency, reducing operational costs and the total cost of ownership.
Databricks on Azure champions next-generation data storage. Azure has gone one step further to introduce a new architecture called Azure Fabric with the concept of OneLake that seamlessly empowers all data workloads from ingestion to consumption.
Organizations must invest in training resources to meet the requirements of in-house expertise for managing data lakes and warehouse integration. Complementary data skills training through a customized curriculum, including change management, will contribute to an efficient and seamless integration.
This change management, together with the adoption of modernized data platforms, contributes to significant improvement in data literacy.
The complex integration requires collaboration between IT teams and business units to prevent disruption to business. Cross-functional collaborations between members of the data engineering, analytics, IT, and business teams will go a long way in ensuring alignment on data strategy and business objectives. While adopting a modern data architecture is the go-to approach to being data-first, the overall strategy will succeed only by consciously promoting a data-first culture among various teams.
Organizations can leverage the scalability and flexibility of data lakes and the structured analytical capabilities of data warehouses to maximize data potential. Integrating data lakes and warehouses allows organizations to effectively utilize their data to derive valuable insights for improved decision-making. Enterprises can leverage data lakes as low-cost storage for all data types and data warehouses for high-performance query and reporting. The integration enables organizations to optimally use data for business decisions and build a data-driven culture.
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