Lakehouse implementation
Our Lakehouse Implementation Services
Data Lakehouse Architecture Design and Consulting
We offer expertise in designing and architecting data lakehouses. This involves understanding the specific needs of the organization, identifying data sources, defining storage and compute layers, and ensuring scalability, security, and performance. And we provide consulting services to guide clients through the process of setting up a well-architected data lakehouse that combines the best features of data lakes and data warehouses.
Data Ingestion and ETL (Extract, Transform, Load):
Data ingestion is a critical step in a lakehouse implementation. We assist clients in ingesting data from various sources (such as databases, APIs, logs, and external systems) into the lakehouse.ETL processes transform and clean the raw data, making it suitable for analysis. We help to set up efficient ETL pipelines to ensure data quality and consistency.
Data Governance and Security
Ensuring data governance and security is essential for any lakehouse. We provide services related to access control, data lineage, auditing, and compliance. We help to define policies, implement role-based access controls, and monitor data access to protect sensitive information.
Data Analytics and Business Intelligence (BI)
We assist clients in leveraging the data stored in the lakehouse for analytics and BI purposes. This includes setting up tools like Apache Spark, Databricks, or Snowflake for querying and analyzing data. They design dashboards, reports, and visualizations to help users gain insights from the data. Customized data processing frameworks are often built to meet specific business requirements.
Custom Application Development and Integration
We develop custom applications that interact with the lakehouse. These applications could be data pipelines, reporting tools, or APIs. Integration with existing systems (such as ERP systems, CRM platforms, or marketing automation tools) is also part of the services offered. This ensures seamless data flow between different parts of the organization.
Functional Testing
How can you benefit from Lakehouse Implementation?
Unified Data Platform
- A lakehouse combines data lakes and data warehouses, providing a unified platform for storing, managing, and analyzing data. Teams can work with raw data for exploration and analytics while still benefiting from structured data for reporting and BI.
Cost-Effectiveness
- By leveraging low-cost storage for raw data (similar to data lakes), a lakehouse reduces infrastructure costs. By leveraging low-cost storage for raw data (similar to data lakes), a lakehouse reduces infrastructure costs.
Schema Evolution
- A lakehouse allows schema-on-read, meaning you can apply schemas when querying data rather than upfront during ingestion. This flexibility accommodates changes in data formats over time without disrupting existing pipelines.
Data Governance and Security
- o The metadata layer in a lakehouse enables better governance, lineage tracking, and access controls. We can enforce policies, audit data usage, and ensure compliance with regulations
How it Works
01. Unified Storage and Compute
- In a data lakehouse, data is stored in a unified storage layer, typically using low-cost object storage (similar to data lakes). This storage layer allows you to store raw, unstructured, or semi-structured data without any predefined schema.
- Unlike traditional data warehouses, where storage and compute are tightly coupled, a data lakehouse decouples storage from compute. This separation enables more flexibility and scalability.
- The compute layer (e.g., Apache Spark, Databricks, or other query engines) processes data directly from the storage layer, allowing you to run analytics, transformations, and machine learning workloads.
03. Data Quality and Governance
- Data quality and governance are critical in a data lakehouse. You can enforce data quality checks, validation rules, and lineage tracking.
- Implementing data governance policies ensures that data is properly cataloged, classified, and secured. It also helps manage access controls and compliance.
- By combining data lineage and metadata management, you gain visibility into data transformations and lineage across the entire data pipeline.
02. Schema Evolution and Data Consistency
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Data lakehouses support schema evolution, meaning you can evolve your data schema over time without disrupting existing workloads. New data can be ingested without requiring immediate schema changes.
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ACID (Atomicity, Consistency, Isolation, Durability) transactions are supported, ensuring data consistency even as you update or modify data.
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This flexibility allows data engineers and data scientists to work with diverse data sources and adapt to changing business requirements.
04. Analytics and Performance Optimization
- Data lakehouses provide a platform for running various analytics workloads, including batch processing, interactive queries, and real-time streaming.
- Optimizing performance involves tuning the compute layer, partitioning data, and leveraging caching mechanisms.
- Materialized views, indexing, and query optimization techniques can enhance query performance, making it easier to analyze large datasets efficiently.
01. Strategy
- Clarification of the stakeholders’ vision and objectives
- Reviewing the environment and existing systems
- Measuring current capability and scalability
- Creating a risk management framework.
02. Discovery phase
- Defining client’s business needs
- Analysis of existing reports and ML models
- Review and documentation of existing data sources, and existing data connectors
- Estimation of the budget for the project and team composition.
- Data quality analysis
- Detailed analysis of metrics
- Logical design of data warehouse
- Logical design of ETL architecture
- Proposing several solutions with different tech stacks
- Building a prototype.
03. Development
- Physical design of databases and schemas
- Integration of data sources
- Development of ETL routines
- Data profiling
- Loading historical data into data warehouse
- Implementing data quality checks
- Data automation tuning
- Achieving DWH stability.
04. Ongoing support
- Fixing issues within the SLA
- Lowering storage and processing costs
- Small enhancement
- Supervision of systems
- Ongoing cost optimization
- Product support and fault elimination.
Why Lakehouse Matters?
Unified Data Repository
Scalability and Flexibility
Cost-Effectiveness
Data Governance and Security
Key Benefits of Lakehouse Implementation
Agility and Speed
Lakehouse architecture accelerates data processing and analysis, enabling faster decision-making and delivering insights in real-time.
Cost Optimization
Efficient use of cloud resources and optimized data storage solutions reduce costs associated with data management and analytics.
Enhanced Data Quality
Centralized data storage and governance mechanisms ensure improved data quality, consistency, and reliability for better decision-making.
Advanced Analytics
Enable advanced analytics, machine learning, and AI capabilities by leveraging a unified data platform for comprehensive insights and predictions.
Why Choose Complere
Infosystem for
Lakehouse Implementation
Assessment and Planning
Comprehensive assessment of your organization's data infrastructure and requirements, followed by a tailored implementation plan.
Architecture and Integration
Designing and integrating a Lake House architecture that aligns with your business goals and integrates seamlessly with existing systems.
Data Migration and Transformation
Efficient and secure migration of data from various sources to the Lakehouse platform, ensuring data integrity and consistency.
Training and Support
Training your teams on Lakehouse usage and providing ongoing support to ensure a smooth transition and optimal utilization of the platform.
