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Data Engineering

Services​

Data Integration​

  • Extract, Transform, Load (ETL) processes to integrate data from diverse sources.
  • Real-time data streaming capabilities.

Data Modeling​

  • Develop and implement data models to structure and organize data efficiently.
  • Leverage industry-standard data modeling techniques (e.g., ERD, dimensional modeling).

Data Warehousing​

  • Build and manage data warehouses for centralized storage and retrieval.
  • Implement cloud-based or on-premises data warehousing solutions.

Big Data Processing​

  • Utilize technologies like Apache Hadoop, Apache Spark, and distributed computing frameworks for processing large-scale data.

Machine Learning Operations (MLOps)​

  • Implement MLOps practices for deploying, managing, and monitoring machine learning models.
  • Develop model versioning, monitoring, and retraining pipelines.

Data Governance and Security​

  • Establish data governance policies and procedures.
  • Implement security measures to protect sensitive data.

Technology Stack​

ETL Tools​

Data Modeling Tools​

  • ERwin
  • IBM Data Architect
  • Microsoft Visio

Data Warehousing​

  • Amazon Redshift
  • Google BigQuery
  • Snowflake
  • Clickhouse
  • Druid
  • Databricks
  • Microsoft Azure Synapse Analytics

Data Analytics​

  • PowerBI
  • Tableau
  • Redash
  • Metabase

Big Data Processing​

  • Apache Hadoop
  • Apache Spark
  • Databricks

MLOps​

  • MLflow
  • Kubeflow
  • TensorFlow Extended (TFX)

Data Governance​

Generative AI​

  • Mixtral
  • LLAMA2
  • LangChain
  • Ollama
  • LM Studio
  • HuggingFace
  • Gemma

SAAS​

State of Data Engineering 2024​

State of Data Engineering 2024

The State of Data Engineering 2024

The State of Data Engineering in India: 2024 – AIM

Roadmaps​

Resources​