Once data has been ingested, it needs to be stored in such a way that it can be easily accessed, transformed and analyzed. This is where data warehouses and data lakehouses come in. These infrastructures form the heart of your Modern Data Stack, centralizing and organizing data to make it usable for analysis and decision-making processes.

Why are these architectures important?

Storing data in an organized and optimized form is essential to guarantee high performance during analytical queries and data processing. A Data Warehouse or Data Lakehouse centralizes all an organization's data, providing a single source of truth for data-driven decision-making. It is also the basis for advanced analysis, BI and AI (generative or machine learning).

Key differences :

• Data Warehouse: A data warehouse is a database optimized for analytical queries and reporting. It is designed for structured data and is particularly effective for rapid analysis of structured data.
• Data Lakehouse: A Data Lakehouse combines the advantages of a Data Warehouse and a Data Lake, enabling structured and unstructured data to be stored in a single environment. It provides both rapid analysis capabilities and the flexibility to handle a variety of data formats (JSON, CSV, images, videos, etc.).

Storage types

The structure of your platform depends on the type of data you manage, and your needs in terms of performance and flexibility. Here are the main types of storage available to build your data infrastructure:

1. Data Warehouse :
   • Storage for structured data (often relational).
   • Optimized for fast queries and in-depth analysis of well-defined datasets.
   • Examples of solutions: Amazon Redshift, Google BigQuery, Snowflake.
2. Data Lake :
   • Storage for unstructured or semi-structured data.
   • Ideal for massive volumes of raw data requiring exploration before transformation and analysis.
   • Examples of solutions: Amazon S3, Azure Data Lake Storage, Google Cloud Storage.
3. Data Lakehouse :
   • Combining the two, it can process both structured and unstructured data, while offering the analytical capabilities of a data warehouse.
   • Examples of solutions : Databricks Lakehouse, Cleyrop.

Existing solutions

There are many solutions on the market for hosting data warehouses and data lakehouses, each with its own strengths depending on an organization's needs.

Open Source tools

• Apache Hive: An open source data warehouse built on top of Hadoop for querying and analyzing large amounts of data. It's ideal for companies managing large volumes of data, although it can be slow for interactive queries.
• Apache Hudi: An open source framework for managing transactional tables on data lakes. Hudi optimizes update and insertion operations in data lakes, while guaranteeing high performance.
• Delta Lake: An open source project developed by Databricks that enables you to implement data lakehouses on data lakes. It guarantees data quality and improves analytical performance thanks to its support for ACID transactions and version management (How to create your data ...).

Proprietary tools

Proprietary solutions are often chosen for their ease of deployment, performance and support.

• Snowflake: Snowflake is a cloud-native data warehouse platform offering real-time analysis capabilities. It is designed to be simple to use, with advanced features such as the separation of storage and computation. Snowflake is particularly appreciated for its flexibility in handling both structured and semi-structured data (JSON, Avro, etc.).
• Amazon Redshift: Amazon Redshift is a managed cloud data warehouse that is part of the AWS suite. It is optimized for massive SQL queries on terabytes of data. It is often used for integration with other AWS services, although it may require expertise in performance optimization.
• Google BigQuery: BigQuery is a serverless data warehouse from Google Cloud, designed for rapid analysis of massive amounts of data. It's ideal for businesses that need to process large volumes of data in a minimum of time.
• Databricks Lakehouse: Databricks combines Data Lake and Data Warehouse capabilities within its platform. Databricks manages both structured and unstructured data with a powerful distributed infrastructure, ideal for machine learning and real-time analysis use cases.

Set-up time and complexity

Setting up a Data Warehouse or Data Lakehouse infrastructure depends on a number of factors, such as the size of the organization, the complexity of the data and in-house expertise. Here's an overview of the typical set-up time for a full deployment:

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Challenges to overcome

• Performance: Guaranteeing high performance for fast analytical queries on large volumes of data is often a challenge, especially for systems handling both structured and unstructured data.
• Scalability: Choosing a solution that can grow with your needs is crucial, especially if data volumes increase or analyses become more complex.
• Cost: Cloud solutions are often billed on a pay-per-use basis, but this can also lead to unforeseen costs if resources are not optimized efficiently.

Why choose Cleyrop?

At Cleyrop, we understand that data warehousing is the key to maximizing the value of your data. Our all-in-one platform enables you to integrate high-performance, secure data warehouses or data lakehouses, while offering advanced data analysis, transformation and governance capabilities. Cleyrop stands out for its flexibility, with full support for structured and unstructured data, as well as seamless integration with ingestion, transformation and generative AI tools.

Our aim is to provide you with a robust, scalable and secure platform, so that you can exploit the full potential of your data while minimizing technical complexity.

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