Big Data & Cloud Data Platforms

Our consultants have spent more than a decade moving very large data sets with Talend, from the on-premises Hadoop clusters of the 2010s to the cloud warehouses and lakehouses that replaced them. In 2026 that work looks like this:

Cloud data platforms

• Loading and transforming data in Snowflake, Amazon Redshift, Google BigQuery, and Databricks with Talend's native components
• ELT pushdown: using Talend to orchestrate transformations that run inside the warehouse rather than row by row in the job engine
• Bulk-load patterns (internal stages and COPY INTO, S3/GCS staging, multi-file parallel loads) that keep warehouse credits under control

Spark and lakehouse

• Talend Spark batch and streaming job design, executed on Databricks, Amazon EMR, or Kubernetes
• Open table formats: Parquet, Apache Iceberg, and Delta Lake
• Partitioning, file-size management, and schema evolution for long-lived lakes

Semi-structured and NoSQL

• MongoDB, DynamoDB, and document-store integration
• JSON and Avro parsing at scale; dynamic-schema ingestion of files with changing layouts

Legacy big-data migration

• Migrating MapReduce, Pig, and Sqoop-era Talend jobs to Spark or warehouse-native ELT. Talend removed MapReduce job generation years ago, Pig components are gone from current Studio, and Apache Sqoop was retired to the Apache Attic in 2021; jobs built on them need to be re-platformed, not patched.
• Hive and HDFS as migration sources: extracting data and metadata from clusters that are being decommissioned

Talend's big-data capabilities now ship within Talend Data Fabric; see the Talend help portal for current Spark and cloud-platform component documentation.

Have a large data set or a legacy Hadoop estate? Contact us.

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