Data Engineering

Sticking to the best practices can increase the likelihood of successful database migration. Some of the practices we follow through a well-planned database migration strategy include,

Focus on Efficiency: Start Small, Scale Smart

Research shows that unforeseen changes are common in data migration projects. In fact, 90% of projects experience adjustments to their initial specifications. To mitigate this risk, we recommend starting with a focused approach. If we’re dealing with multiple databases, we consider migrating a few subset of them first. This allows us to test the approach and gain confidence before tackling a larger scope.

Understanding Your Data Landscape

Before embarking on the database migration journey, it’s essential to take stock of the existing data. This includes factors like the type and volume of information you’re working with, the operating systems involved, and the source and target database platforms.

By analyzing your data landscape, we can identify potential opportunities for optimization. For instance, we might discover outdated records that can be safely archived or removed. Additionally, we can proactively address any compatibility challenges that might arise when migrating between different database structures (heterogeneous migration), such as relational (e.g., Oracle) and non-relational (e.g., MongoDB) systems.

Seamless Migration

We simplify the complexities of zero-downtime database migration. Forget managing intricate details; let us handle the rest. The data experts evaluate and create initial schemas, loads your historical data, and establishes continuous synchronization between your systems – ensuring your databases stay in sync without interrupting ongoing operations.

Built by Database Experts, Designed for You

The process is developed by a team with extensive experience in data management and migration for global enterprises. We tackle common and complex migration challenges, empowering you to focus on your core business objectives.

Are you looking to modernize your data warehouse and unlock valuable insights? 

Traditional data warehouses struggle to keep up with today’s data growth and advanced analytics demands. BigQuery offers a solution designed to scale quickly and cost-effectively while providing blazing-fast, real-time, and predictive insights. BigQuery is a truly serverless data warehouse built on Google’s powerful infrastructure, capable of handling large data volumes effortlessly.

Accelerate Your Modernization Journey

Transcloud specializes in migrating data warehouses to BigQuery, leveraging deep expertise and best practices. We offer a proven migration framework to accelerate your time to value and future-proof your analytics investment. Jump-start your modernization journey with Transcloud’s data engineering services and harness the power of BigQuery and the ecosystem’s features.

Unlock Insights and Scale Your Analytics

BigQuery is renowned for its scalability, powerful features, and cost-effectiveness, making it ideal for businesses of all sizes. Our comprehensive toolset and services make it a seamless experience to modernize your legacy data warehouse.

Data Lake Modernization involves optimizing and enhancing data lakes to maximize their efficiency, scalability, and usability within an organization’s data ecosystem. A Data Lake serves as a centralized repository for storing, ingesting, transforming, analyzing, and modeling data in a secure, cost-effective, and easily manageable manner. It complements Data Warehouses by integrating or coexisting with them to leverage the strengths of each solution.

Migrate Apache Spark and Hadoop-based Data Lakes to Google Cloud

• Fully Managed Services: Quickly provision, auto-scale, and govern purpose-built data and analytics clusters using services like Apache Spark on Google Cloud.
• Integrated Data Science and Analytics: Leverage a comprehensive suite of tools including Apache Spark, BigQuery, AI Platform Notebooks, GPUs, and other accelerators for streamlined analytics development and deployment.
• Cost Management: Optimize costs with Google Cloud’s auto-scaling services that decouple storage from compute, allowing for faster query speeds and cost management at a per-gigabyte level. Its lower cost compared to on-premises Hadoop deployments using custom machine configurations, automated cluster deletion, and other cost-saving measures.

By leveraging these strategies and harnessing Google Cloud’s powerful platform, Transcloud empowers organizations to modernize their data lakes, accelerate analytics development, and achieve substantial cost efficiencies compared to traditional on-premises deployments. Our expertise in data lake modernization enables us to guide clients through a seamless transition, ensuring optimal utilization of Google Cloud’s capabilities for actionable insights and strategic decision-making. 

Streaming analytics is about processing and analyzing the data records continuously in realtime or near-realtime rather than in batches. Commonly, streaming analytics is useful for the types of data sources that send data in a continuous flow as the data is generated. It includes a wide variety of data sources, such as telemetry from connected devices, log files generated by customers using web applications, e-commerce transactions, user behavioural data or information from social networks or geospatial services. It’s often used for real-time aggregation and correlation, filtering, or sampling.

Transcloud specializes in streamlining data processing on the Google Cloud Platform (GCP) for diverse industry verticals. Our expertise encompasses a range of data processing solutions tailored to enterprise needs. We assist organizations in leveraging robust ecosystem to drive advanced data analytics, from efficient data ingestion to building modern data warehouses and orchestrating analytics platforms.

Our comprehensive approach explores the spectrum of data processing solutions available on GCP, covering essential aspects from data ingestion at scale to architecting modern data warehouses and orchestrating analytics platforms. 

Transcloud facilitates a successful data analytics journey by emphasizing core components that drive effective data processing,

Data Ingestion: Efficiently ingest data from various sources. The event-driven mechanism guarantees reliability and scalability, which can handle massive data volumes.

Data Processing: Cloud Dataflow is a reliable option for processing real-time streaming data. It offers a single programming model which makes managing workloads and operations simpler compared to traditional approaches. Additionally, Apache Beam is an open-source framework that allows you to define and execute data pipelines using different programming languages.

ETL Workflows: We use Cloud Data Fusion to easily build and manage enterprise data integration pipelines. Data engineers and scientists can visually create, test, and deploy pipelines, enhancing operational efficiency and scalability.

Workflow Orchestration: Transcloud has implemented end-to-end data pipeline orchestration with Cloud Composer, based on Apache Airflow. This fully-managed service allows for graphical representation of workflows and seamless integration with GCP’s data and analytics services.

Elevate Your Data Processing Strategy

Transcloud specializes in helping organizations harness the full potential of Google Cloud for data analytics and streamlining data processing workflows. If you’re thinking of moving your pipelines to the cloud or starting new projects, our team of experts can help you modernize your data processing, speed up your analytics development, and reduce costs compared to traditional legacy solutions.