Sii Ukraine
SII POLAND
SII SWEDEN
Sii Ukraine
SII POLAND
SII SWEDEN
Sii Ukraine
SII POLAND
SII SWEDEN
With Azure, AWS, Snowflake, and Databricks, we offer your company scalable and secure data solutions for robust management, including analytics and real-time insights.
Make your business operate more effectively with the Microsoft Azure data platform. Certified Microsoft experts at Sii will enable a smooth data strategy transformation with modern tools:
Reach out for auto-scaling, secure, and cost-efficient data management. AWS Data Platform is a serverless solution with deep integrations for AI, analytics, and real-time processing. Our AWS specialists will help you with implementing:
When you need a cloud-native data warehousing solution for effective data management across multiple cloud providers (AWS, Azure, GCP) our experts will support you in Snowflake data platform implementation.
With Snowflake you will get:
If your business needs advanced analytics and big data processing, we offer Databricks data platform implementation. Databricks integrates effectivly with cloud environments, providing a unified analytics platform that:
Our team includes over 300 skilled data professionals, from Solution Architects to Data Engineers. With a wide range of expertise and years of experience, we handle any data challenge with precision and innovation, delivering insights that drive real business outcomes.
We offer complete support for your data platform needs, from initial solution assessment and architecture design to DevOps, implementation, and ongoing maintenance. This ensures smooth integration and top performance throughout the data lifecycle.
Our expertise covers the latest cloud technology and reporting tools including Microsoft Azure, AWS, Google Cloud Platform, Snowflake, or Informatica Cloud. In-depth technology knowledge combined with experience in various industries, from finance to manufacturing, allows us to tailor solutions that meet and exceed your business needs.
See how we do it step by step
Read out FAQ
Modern Data Platforms are integrated solutions designed to be a central Data Hub for company. They handle the collection, storage, processing, and analysis of large volumes of data. Modern Data Platforms should provide scalability, flexibility, and advanced analytics capabilities. They often leverage cloud infrastructure. Advanced Data Platform offer real-time data processing, machine learning, and robust data governance.
Modern data platforms offer numerous benefits to businesses, like:
• Enhanced decision-making
• Cost savings, scalability and flexibility
• Improved operational efficiency
• Data security and compliance
• Innovation and competitive sdvantage
A modern data platform significantly enhances decision-making through several key mechanisms:
• Real-time insights through continuous data processing. Modern data platforms process data as generated, providing real-time analytics and insights. This allows businesses to react swiftly to changing conditions, such as market trends or operational issues, leading to more timely and effective decisions.
• Integrated data sources through unified data view. Modern data platforms integrate data from various sources to create a single source of truth. This holistic view ensures that decisions are based on comprehensive and accurate data.
• Advanced analytics through predictive and prescriptive analytics. Modern data platforms come with tools that use machine learning and artificial intelligence. This enables proactive decision-making and strategic planning.
• Data visualization through intuitive dashboards and reports. Modern data platforms transform complex data into easily understandable graphs, charts, and dashboards. These visual aids help stakeholders quickly grasp insights and make informed decisions.
• Collaborative tools through shared workspaces. Platforms like Azure Fabric, Snowflake, and Databricks provide collaborative environments. Data scientists, analysts, and business users can work together. This collaboration aligns insights with business objectives and ensures that decisions are well-informed.
• Streamlining the decision-making process through automated workflows. Modern data platforms, with their automated ETL processes, significantly reduce the risk of human error and expedite the decision-making process. Automated workflows ensure that data is readily available for analysis when needed.
Modern data platforms are designed to provide robust scalability and flexibility. This is critical for managing the ever-growing and evolving data needs of businesses. They achieve this through:
• Elastic cloud infrastructure:
Modern data platforms in the cloud, like AWS, Azure, and Google Cloud, can dynamically allocate computing resources based on demand. This elasticity allows businesses to scale up during peak loads and scale down during off-peak times, optimizing cost and performance. Some platforms offer serverless options where the cloud provider fully manages the underlying infrastructure, allowing automatic scaling without any manual intervention.
• Separation of storage and compute:
Platforms like Snowflake, by separating storage and compute resources, offer businesses the ability to expand their data storage capacity without affecting compute resources and vice versa. This not only provides cost efficiency but also operational flexibility, two crucial factors for any business.
• Distributed processing:
Technologies like Apache Spark and Hadoop, often integrated into modern data platforms, enable distributed data processing across multiple nodes. This parallelism enhances the platform’s ability to handle large datasets and complex computations efficiently. Modern data platforms can manage clusters of computing resources, automatically adding or removing nodes based on workload demands to ensure optimal performance and resource utilization.
By incorporating these features, modern data platforms ensure businesses can handle increasing data volumes and complexity while maintaining the flexibility to adapt to new requirements and technologies. This scalability and flexibility are essential for staying competitive in a data-driven world.
Modern data platforms enhance operational efficiency through cloud infrastructure in several key ways:
These platforms provide a flexible, scalable, and secure foundation for efficient data management.
Modern data platforms are key to driving innovation and gaining a competitive edge because of:
By leveraging these capabilities, modern data platforms help businesses innovate, operate efficiently, and stay competitive.
Use cases include customer analytics, operational efficiency improvement, fraud detection, predictive maintenance, financial analysis, market research, and personalized marketing.
Enterprise data platforms aggregate and analyse data from various sources, providing insights and that help businesses make informed decisions. Businesses can than understand trends and customer behaviour, optimize operations, improve products and services and enhance customer experiences, and drive strategic decisions. It eliminates data silos and enhances collaboration.
The Azure data platform enhances business operations by offering a comprehensive suite of tools and services, such as Azure Fabric for data integration, scalable storage, visualization, and dashboarding in Power BI and AI capabilities. These services help businesses gain insights quickly, improve decision-making, and streamline operations by automating data workflows and providing real-time analytics. Other tools are Azure Synapse Analytics for data integration and analysis, Azure Data Lake for scalable storage and Azure Data Factory for creating ETL pipelines.
The AWS data platform provides a wide range of solutions including Amazon Redshift for data warehousing, Amazon S3 for scalable storage, AWS Glue for ETL (Extract, Transform, Load) processes, Amazon EMR for big data processing, and Amazon Kinesis for real-time data streaming. These tools help businesses manage, process, and analyze data efficiently, enabling quick insights and enhanced operational efficiency.
Snowflake provides a cloud-based data warehousing solution with near-infinite scalability, automatic scaling, and separation of storage and compute, making data management more efficient and cost-effective. It is a Platform as a Service solution (PaaS) that can be deployed on all the biggest cloud providers’ infrastructures like Azure, AWS, or GCP. Snowflake separates storage and compute resources, allowing for scalablcomputinge and cost-effective data processing. It provides robust data-sharing capabilities, seamless integration with various data sources, and support for diverse data types. Snowflake’s architecture ensures high performance, concurrency, and data security, simplifying data operations and management.
The Databricks data platform, built on Apache Spark, offers several benefits including unified analytics for data engineering, data science, and machine learning. It provides collaborative notebooks, automated cluster management, and scalable processing power. Databricks enhances productivity by integrating various data sources and tools, enabling real-time analytics, and simplifying the development and deployment of machine learning models.
Steps include assessing data needs, choosing the right platform, designing the architecture, setting up data integration and ETL processes, implementing data governance, and continuous monitoring and optimization.
Common components include data storage (data lakes and warehouses), data integration tools (ETL/ELT), data processing engines, analytics and visualization tools, data governance frameworks, and security measures.
ETL stands for Extract, Transform, Load. It is a process that extracts data from various sources, transforms it into a suitable format, and loads it into a data warehouse or data lake. ETL ensures that data is consolidated, cleansed, and prepared for analysis. ETL process might use different technologies and techniques like Change Data Capture or real-time processing.
Big Data refers to extremely large, complex datasets that cannot be processed using traditional data processing tools. It is often stored as raw, unstructured data in its native format in a Data Lake. Data lakes enable organizations to collect and store vast amounts of diverse data types, which can later be processed and analyzed using big data processing frameworks like Apache Hadoop and Apache Spark.
Change Data Capture (CDC) is a technique used to identify and capture changes made to data in a database ensuring minimal impact on source systems. It is used to keep data warehouses, data lakes, and other systems synchronized with the latest changes, enabling real-time data integration and analytics.
Real-time data processing involves analyzing data as it arrives, enabling immediate insights and actions, which is crucial for time-sensitive applications like fraud detection and live customer interactions. It involves continuously capturing, processing, and analyzing data as it is generated. Technologies like stream processing frameworks (e.g., Apache Kafka, Apache Flink) enable immediate insights and actions based on up-to-date information. Serverless architecture allows developers to build and run applications without managing infrastructure. In real-time data processing, serverless frameworks (e.g., AWS Lambda, Azure Functions) automatically scale to handle varying workloads, reducing latency and operational complexity.
Organizations integrate IoT data with data platforms by using edge computing for initial data processing, leveraging stream processing frameworks for real-time analytics, and storing data in data lakes or warehouses for further analysis. They also ensure secure data transmission and implement scalable architectures to handle the high volume and velocity of IoT data.
Data platforms provide the infrastructure for storing and processing large datasets required for machine learning and AI. Integrated tools and frameworks (e.g., TensorFlow, PyTorch) allow data scientists to build, train, and deploy models that automate decision-making and generate predictions. They also offer tools for data preprocessing, model training, validation, and deployment, as well as scalable computing resources to handle complex computations.
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