Data Engineering

Welcome to the nexus of robust, scalable, and efficient data solutions. Our Data Engineering services architect, curate, and maintain the backbone of your data-driven initiatives. Navigate the intricacies of data with our experts, and let’s lay the foundation for your analytics success together.

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Google Cloud Platform

Leverage Google BigQuery’s scalability and Cloud Functions for event processing. Automate transfers with Transfer Services, and apply Compute Engine’s custom scripting for tailored transformations to enhance data management efficiency and agility.

Snowflake

Snowflake revolutionizes advanced data transformation by offering a cloud-based, elastic architecture. It enables seamless scalability, performance optimization, and support for diverse data sources. With Snowflake, you can achieve sophisticated data shaping, aggregation, and enrichment.

Microsoft Azure

Experience Azure’s integrated services: scalable data storage with Azure Blob and Data Lake Storage, ETL orchestration using Data Factory, and SQL querying through SQL Pool. This synergy streamlines data movement, transformation, and analysis, enhancing tailored and efficient data engineering for informed decision-making.

Amazon Web Services

Leverage AWS’s toolkit: S3 and Athena for storage and querying, Glue jobs for ETL, and IP/Elastic IP/ELB for networking. These synergize within Amazon Web Services for streamlined data handling, transformation, and networking. Empower efficient data engineering, seamless analytics, and tailored application deployment.

Efficient Data Ingestion ETL Pipelines

Cloud data integration streamlines the merging of diverse data into cloud storage or apps, leveraging cloud services for seamless movement and transformation. Our Team excels at implementing and managing efficient, scalable Cloud Data Warehouses, ensuring optimal integration and performance for your data needs.

Key Benefits

Improved Decision-Making

Integrated data provides a comprehensive view of an organization's operations, enabling better-informed decision-making.

Efficiency

Data integration automates data transfer and transformation, saving time and reducing manual data handling.

Cost Reduction

It lowers operational costs by streamlining data management processes and reducing the need for redundant data storage.

Business Insights

Integrated data allows for more advanced analytics and reporting, uncovering valuable insights for optimizing processes and strategies.

Microsft Azure

Tap into Azure’s capabilities with Azure Data Factory, ADLS, and Azure Synapse Analytics for advanced data transformation. Utilize Data Factory for ETL orchestration, ADLS for scalable storage, and Synapse Analytics for intricate querying and analytics. Seamlessly manage, process, and analyze data to achieve sophisticated transformations and meaningful insights within the Azure environment.

Google Cloud Platform

Leverage GCP’s potential with Google BigQuery, Cloud Dataflow, Pub/Sub, and Cloud Functions for advanced data transformation. Optimize operations with BigQuery’s robust querying, Dataflow’s efficiency, Pub/Sub’s real-time streaming, and Cloud Functions’ event-driven actions. Empower sophisticated data shaping, streaming, and automation within the Google Cloud Platform.

Python

Experience the power of Python, along with libraries like Pandas, R, and Pandas Profiling, to elevate Data Quality Assurance. Leverage Pandas for streamlined data manipulation, harness R for statistical insights, and utilize Pandas Profiling for comprehensive data analysis. This ensemble empowers you to thoroughly assess data quality, ensuring dependable accuracy and consistency, thus enhancing your decision-making processes with confidence.

ETL Toolkit

Unlock the potential of ETL tools like Pandas, Apache Spark, and Python scripting for advanced data transformation. Utilize Pandas for flexible manipulation, harness Spark’s efficiency with large datasets, and leverage Python scripting for tailored processing. Achieve intricate data shaping, enrichment, and informed decision-making tailored to your needs.

Advanced Data Transformation

Advanced-Data Transformation refers to a set of sophisticated techniques and processes used to convert and manipulate data from one format or structure into another, typically with the goal of making the data more suitable for analysis, reporting, or other specific purposes.

Key Benefits

Improved Data Quality

Data Normalization

Efficient Data Aggregation

Enhanced Data Compatibility

Python

Data Smoke Testing + A/B Testing

Experience enhanced Data Quality Assurance through Data Smoke Testing and A/B Testing. Data Smoke Testing swiftly detects major errors, while A/B Testing compares data versions for discrepancies. These techniques guarantee accurate and reliable data, bolstering your confidence in data quality for better-informed decision-making.

Python

Data Quality Assurance

DQA is a comprehensive process and set of practices aimed at ensuring that data used within an organization is accurate, reliable, consistent, and fit for its intended purpose. It involves measures, methodologies, and tools that help maintain and improve the quality of data throughout its lifecycle. Convz Team is working with best-in-class partners such as Informatica, Microsoft Azure, Collibra and Alation. Reach out to us for more information.

Key Benefits

Better Customer Insights

High-quality data provides more accurate insights into customer behavior, preferences, and trends. This information can be used to tailor marketing campaigns, product development, and customer engagement strategies.

Data Governance

Implementing DQA often goes hand-in-hand with establishing robust data governance practices

Data-driven Innovation

Reliable data forms the foundation for data-driven innovation, enabling organizations to experiment with new business models, products, and services.

Jenkins

Jenkins allows you to automate and schedule a wide range of data-related tasks, such as ETL processes, data transformations, and data loading. By creating pipelines or workflows, it streamlines repetitive tasks, reduces manual intervention, and enhances efficiency in managing, processing, and moving data.

Airflow

Airflow offers a robust framework for designing, scheduling, and overseeing intricate data workflows. Its directed acyclic graph (DAG) design empowers orchestration of ETL operations and data transformations. This automation boosts efficiency, guarantees reliability through reproducibility and monitoring, enabling Data Engineering teams to optimize pipelines and enhance overall productivity.

GCP Scheduled Query

Embedded within the Google Cloud Platform, Scheduled Query empowers effortless automation of data retrieval and processing. With the ability to schedule SQL queries at designated intervals, it automatically stores or transforms the query outcomes. This streamlined automation streamlines common data operations like aggregation, summarization, and enrichment, eliminating manual effort.

AWS Data Transfer Service

AWS Data Transfer Service streamlines process automation by facilitating secure and seamless data movement between various AWS services. It automates the transfer of data across diverse sources, enhancing efficiency and reducing manual intervention. This enables you to focus on higher-value tasks while ensuring reliable, automated data flow for your data engineering workflows.

Azure Data Migration Service

Azure’s Data Migration Service streamlines Process Automation by offering a seamless platform for migrating on-premises databases to Azure. It automates tasks like schema and data transfer, reducing manual effort. Through its guided workflow and monitoring, it simplifies the migration process, ensuring data integrity and accelerating your data engineering initiatives.

GCP Storage Transfer Service

GCP Storage Transfer Service enhances Process Automation by enabling automated, scheduled transfers of data between on-premises systems and Google Cloud Storage. It simplifies the movement of large datasets with minimal manual intervention. By setting up recurring transfers, it optimizes data workflows, improving efficiency and data availability in your data engineering operations.

Optimized Data Ops Process & Automation

OPA involves the use of advanced automation techniques and data-driven approaches to streamline and enhance data engineering processes. The Convz Team can provide several case studies on the same across Google Cloud, Azure, and Snowflake projects.

Key Benefits

Data Ingestion and Integration Optimization

We can automate the discovery, ingestion, and integration of data from diverse sources. It can dynamically adapt to changes in data source formats, schemas, and availability, ensuring that data engineering pipelines remain up-to-date.

Data Storage Optimization

We can automatically manage data partitioning, compression, and archival processes to reduce storage costs while maintaining data accessibility.

Monitoring and Performance Optimization

We use monitoring and performance optimization tools to identify and address inefficiencies in data engineering processes, ensuring that data pipelines run smoothly.

Jenkins

Jenkins provides automated monitoring of ETL workflows, instantly detecting any failures or issues. Through customizable alerts and notifications, Jenkins ensures that you’re promptly informed about any problems, allowing you to take immediate action, maintain data integrity, and ensure the smooth operation of your data pipelines.

Airflow

Airflow offers a robust framework for designing, scheduling, and overseeing intricate data workflows. Its directed acyclic graph (DAG) design empowers the orchestration of ETL operations and data transformations. This automation boosts efficiency, and guarantees reliability through reproducibility and monitoring, enabling Data Engineering teams to optimize pipelines and enhance overall productivity.

Cloud Logging

Cloud Logging, part of Google Cloud’s suite of services, is a robust solution for monitoring ETL processes, allowing you to collect, store, and analyze log data from various sources, including data pipelines. This way, you can detect anomalies, errors, or failures in your ETL workflows and can swiftly respond to issues, maintain data pipeline reliability, and ensure optimal performance within the Google Cloud environment.

Azure Monitor

By using Azure Monitor – a comprehensive solution from Microsoft for monitoring and managing ETL processes – you’re able to track the performance and health of your data pipelines in real-time, proactively identifying and addressing any issues and hence ensuring the reliability, efficiency, and integrity of your data workflows

Real-Time Monitoring & Logging

Explore with us how the Convz Data Engineering team can help you start tracking and recording events, data pipelines, and activities in real-time across computer systems, applications, networks, and processes.

Key Benefits

Alerting and Notification

We can trigger alerts and notifications when predefined thresholds or conditions are met, ensuring that the right personnel are notified promptly to take action.

Immediate Issue Detection

That allows organizations to detect issues, errors, or anomalies as soon as they occur. This enables rapid response and issue resolution, minimizing downtime and service disruptions.

Capacity Planning

By tracking resource usage and application performance, organizations can make informed decisions about scaling infrastructure and planning for future capacity needs.

Industry Partnerships

Hear from our clients

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