Cloud Mass Ingestion
Efficiently ingest streaming big data, databases, and files with cloud-based services.
Data ingestion at the speed of business
Today’s analytics systems are hungry for data. To get the most complete analytics results, you must start by quickly and accurately ingesting large amounts of information. Informatica’s cloud-based services efficiently ingest data into on-premises systems, cloud repositories, and messaging hubs like Apache Kafka so it’s quickly available for real-time processing. Plus, you’ll get support for streaming IoT and log data, large file sizes, and change data capture for databases.
Why do I need cloud mass ingestion?
To efficiently move streaming data, IoT data, file data and databases onto cloud data lakes, making it easily accessible for analytics and data science initiatives.
To ingest change data capture (CDC) data onto cloud data warehouses such as Amazon Redshift, Snowflake, or Microsoft Azure SQL Data Warehouse so you can make decisions quickly using the most current and consistent data.
To accelerate ingestion of real-time data from logs and clickstreams onto Kafka to better support microservices and real-time event processing.
Make more data available for analytics with Informatica mass ingestion services
Informatica offers three cloud-based services to meet your specific data ingestion needs. Each managed and secure service includes an authoring wizard tool to help you easily create data ingestion pipelines and real-time monitoring with a comprehensive dashboard.
Database Ingestion
Ingest data from relational databases including Oracle, Microsoft SQL Server, and MySQL
Address change data capture needs and get support for schema drift
Ingest data onto Amazon S3, Kafka, Microsoft Azure Data Lake Storage, Microsoft Azure SQL Data Warehouse, or Snowflake
Support three ingestion modes: initial load (one time), incremental load (continuous), or initial plus incremental
File Ingestion
Transfer any size or type of file with high performance and scalability
Support major protocols including Advanced FTP, SFTP, and FTPS; Amazon S3; Microsoft Azure Blob and Azure Data Lake Storage; Google Cloud Storage; and HDFS
Support multiple data targets including FTP, SFTP, and FTPS; Amazon S3 and Redshift; Microsoft Azure Blob and Azure Data Lake Storage; Google Cloud Storage and BigQuery; HDFS; and Snowflake
Streaming Data Ingestion
Collect, filter, and combine data from streaming and IoT endpoints and ingest it onto your data lake or messaging hub
Support data sources such as logs, clickstream, social media, Kafka, Amazon Kinesis Data Firehose, Amazon S3, Microsoft Azure Data Lake Storage, JMS, and MQTT
Support data targets including Kafka; Amazon S3 and Kinesis Data Firehose; and Microsoft Azure Event Hubs