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Cloud-Based Data Architecture

Data Lakes

Data lakes are a core component of cloud-based data architectures providing scalability, cost efficiency, data integration, advanced analytics, artificial intelligence support and real-time data processing. Using data lakes, our customers can store and manage massive amounts of diverse data, break down silos and enable advanced data analysis and decision-making. 
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How to Implement Data Lakes in Cloud-Based Architecture?

As a C&F, our first focus is data acquisitionusing both batch processing tools as well as stream processing services to handle real-time data. For data storage, we use scalable cloud platforms that offer flexible schema reading capabilities, allowing us to store raw data in its native format. We use data catalog tools to manage and discover our data assets effectively. This helps to track the origin of data, itsquality and manage its lifecycle. In terms of data processing and transformation, we use tools to transform raw data into a structured format suitable for further analysis. This is done thanks to distributed processing engines that provide us with efficient processing of large data sets. Finally, we integrate collected data with analytics and visualization tools to produce useful insights for our clients.

Scalability and flexibility

Take advantage of unlimited scalability and flexibility to store and manage large volumes of different types of data without pre-defined schemas.

Cost efficiency

Use payment models according to actual usage, optimizing costs and reducing the need for significant investment in physical infrastructure to allocate resources more efficiently.

Advanced analytics and AI capabilities

Support advanced analytics and AI initiatives by providing a rich source of diverse data that can be processed and analyzed, driving innovation and competitive advantage.

Real-time data processing and decision-making

Capture and process data in real-time, facilitating rapid insights and decision-making to stay competitive in the marketplace.

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For us, data lakes are an essential part of our offering because they provide virtually unlimited scalability and flexibility, allowing us to handle growing volumes of data without significant infrastructure changes. Cloud storage services enable us to scale storage flexibly, and save raw data in its native format, adapting to different types of data. They are also more cost-effective, using payment models according to actual usage to optimize expenses. The ability to consolidate data from various sources into a single, unified repository eliminates data silos and makes it easier to access and analyze data. Moreover, data lakes support advanced analytics and artificial intelligence, providing a rich source of insights that can be leveraged by the business. Finally, processing of data streams is critical for real-time acquisition,processing and decision-making.

Overview

A data lake is a centralized repository capable of storing massive amounts of raw data in its native format. Data stored in a data lake can be structured, semi-structured, and unstructured data. Unlike data warehouses, which store data in files or folders, data lake architecture is flat and uses object storage to store data with metadata tags. By storing all of an organization’s data in a single location without the need to impose a schema, as with a data warehouse, data lakes provide a secure platform that can ingest any type of data quickly. With our customized Data Lake solutions, you can harness the scalability, flexibility, and cost-efficiency of data lakes for advanced data analytics and real-time data processing for rapid insights and decision-making. 

Experts in the field

Michał Majkowski
Principal Software Architect
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See Technologies We Use

At the core of our approach is the use of market-leading technologies to build IT solutions that are cloud-ready, scalable, and efficient. See all
Trino
Snowflake
Delta Lake
Databricks Delta Lake
Azure Blob Storage
Apache Spark
Apache Iceberg
Apache Hadoop
Amazon S3
Apache Airflow

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