Many large-scale organizations continue to use legacy data warehouses for data management and reporting processes. These warehouses are deeply connected within an enterprise’s data infrastructure, collecting data from multiple sources and storing it in a centralized on-premises repository. However, despite its accumulation and consolidation capabilities, the legacy warehouses (built and hosted on outdated architectures) are highly vulnerable to the single point of failure and cyber attacks. A single external threat might result in massive data losses and operational inefficiencies.
In addition, the legacy data warehouses lack automated data extraction transformation and loading (ETL) capabilities. Business users or analysts need to manually embed extensive codes and scripts within the warehouses to extract, transform, and load data. This manual approach is time-intensive, thereby hindering real-time analytics and decision-making abilities.
Pentaho Data Lakes Integration– The Best Way to Modernize Data Warehouses
To make legacy warehouses fault-tolerant and ETL automation-friendly, integrating cloud-powered data lakes into them is an effective approach. Pentaho Data Integration (PDI) is a cloud-based orchestration tool that offers a range of data lake templates that can be configured and deployed in a live data infrastructure. These templates don’t require a defined schema to store, extract, transform, and load data. Hence, when configured and integrated, Pentaho data lakes consistently process massive structured/unstructured datasets accumulated by warehouses and generate valuable insights instantly. Developers from a reputable Pentaho services provider effectively configure and integrate data lakes with warehouses through REST APIs and event-driven connections.
Besides, the Pentaho data lakes are designed using a distributed cloud architecture that stores processed data across multiple nodes/servers. This lessens the risk of a single point of failure in legacy data warehouses.
Some of the key functionalities offered by Pentaho-built data lakes include:
Real-Time Analytics – Pentaho data lakes facilitate real-time data processing and analytics, enabling business users to perform rapid analysis on streaming data within warehouses. This is beneficial during real-time decision-making scenarios, such as operational monitoring, fraud detection, and customer engagement.
Flexible Data Storage – As stated before, the distributed cloud architecture enables Pentaho-built data lakes to store huge volumes of data in various formats. Hence, by transferring non-critical or less frequently consumed data in warehouses to these data lakes, storage costs can be reduced significantly.
Data Security and Governance – The Pentaho community has built data lake templates with robust encryption, auditing, and access control mechanisms. These functionalities ensure that the warehouse data transformed and stored within them remains compliant with security and regulatory requirements.
Use Cases of Pentaho Data Lakes and Legacy Warehouse Integration
Tactical Reporting
Legacy data warehouses with batch (scheduled) processing methodology often face challenges in generating instant data reports for tactical decision-making. However, when Pentaho data lakes are integrated, such warehouses support real-time data ingestion and processing, offering updated information for tactical reporting. In other words, warehouses generate reports that reflect the most recent data by leveraging historical data for trend and context analysis.
Moreover, after integration, Pentaho solutions providers enable easy transferal of complex reporting and querying tasks from legacy warehouses to data lakes. This hybrid approach optimizes the query processing efficiency and reporting speed while reducing the load on the legacy warehouse system.
Natural Language Processing
Several enterprises are looking to improve customer service through natural language processing (NLP) models. These models perform quick analysis and offer better growth opportunities for sales and marketing teams. A data warehouse organizes massive unstructured/structured data related to customers and clients. This data is provided as input for training NLP models, which enables chatbots or customer relationship management systems (CRM) to deliver real-time responses.
However, to ensure quality outcomes, input data for NLP models requires consistent cleaning and standardization. By integrating Pentaho data lakes with warehouses, the process of data cleaning, standardization, and exporting to the models can be easily automated. The intuitive ETL functionality enables data lakes to continually pre-process, transform, and transfer data to the NLP models. By leveraging well-processed data inputs, the NLP models deliver faster and more accurate results.
Auditing and Compliance
The siloed nature of legacy warehouses makes it difficult for analysts and business users to retrieve data and perform audit checks effectively. By integrating Pentaho data lakes with the warehouses, the process of auditing and compliance maintenance becomes easier. The data lakes automatically create metadata for every processed and transformed data. This metadata enables business users to monitor data lineage (origin, transformations, and usage) effectively and with ease.
Additionally, data lakes equipped with ETL automation capabilities aid in the classification of diverse datasets within warehouses according to compliance requirements. This classification is crucial for identifying and removing duplicate datasets and resolving potential data compliance issues before they intensify. In short, data lakes integration minimizes the possibility of warehouse silos and improves data management efficiency.
Closing Thoughts
Configuring and integrating cloud-based Pentaho data lakes with a legacy data warehouse is a complex and overwhelming endeavor for in-house IT teams. It requires a deep understanding of both the existing legacy warehouse infrastructure and the data lake’s storage/security policy configuration techniques. A minor misconfiguration can result in data storage inconsistencies and losses. To overcome these scenarios, organizations should consider collaborating with a recognized Pentaho data services. The dedicated Pentaho experts configure the data lake templates according to business requirements and implement appropriate encryption controls to prevent losses.
Moreover, through a comprehensive analysis, experts understand the endpoints, dependencies, and compatibility requirements of a functional legacy data warehouse. Based on this analysis, they decide the right integration approach (REST API or Point-to-Point) and connect the configured data lakes with the data warehouse. This strategic approach ensures that both legacy data warehouses and newly integrated data lakes remain highly operational in the long term. By utilizing data warehouses and data lakes concurrently, analysts and data engineers obtain real-time insights and drive technical and business improvements, thereby achieving higher returns on investments (ROI).
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