Data Ops

As data becomes the driving force behind modern businesses, managing and processing vast amounts of information has become a challenge for many organizations. DataOps, an agile and collaborative approach to data management, has emerged as a solution to address these challenges. At ISOTRPIC, we specialize in providing end-to-end DataOps services, helping you optimize data pipelines, improve data quality, and accelerate time-to-insight by leveraging the right tools and frameworks.

Our team of experienced DataOps consultants will work closely with your organization to develop a tailored DataOps strategy, ensuring a smooth and efficient transition to a modern data management approach. Our consulting services include:

DataOps maturity assessment and roadmap development
Tool and framework selection and integration
Data governance and compliance best practices
Establishing metrics and KPIs for measuring DataOps success
Data pipeline optimization and architecture review

We will design, configure, and implement robust data pipelines tailored to your data sources, processing requirements, and business objectives. Our data pipeline implementation services cover:

Data ingestion from various sources (databases, APIs, streaming, etc.)
Data transformation and cleansing using popular ETL/ELT tools
Data storage and processing using modern data platforms (data lakes, data warehouses, etc.)
Integration with analytics, visualization, and machine learning tools
Monitoring, logging, and alerting setup for data pipelines

Our DataOps experts are proficient in a wide range of tools and frameworks, allowing us to cater to diverse technology stacks and project requirements. Some of the tools and frameworks we work with include:

Data Integration Tools: Apache NiFi, Talend, Informatica, and Flink
ETL/ELT Tools: Apache Beam, Datastage, Azure Data Factory, and AWS Glue
Data Platforms: Hadoop, Apache Spark, Amazon Redshift, and Snowflake
Data Storage: Amazon S3, Google Cloud Storage, Azure Blob Storage, and HDFS
Data Processing Engines: Presto, Hive, Impala, and Databricks
Data Governance: Apache Atlas, Collibra, Alation, and AWS Lake Formation
Data Quality: Trifacta, DataRobot, Great Expectations, and dbt
Data Visualization: Tableau, Power BI, Looker, and Apache Superset
Machine Learning Frameworks: TensorFlow, PyTorch, Scikit-learn, and MLflow