Apache Sqoop is a convenient solution for enterprises working with data lake initiatives. Designed for bulk transfer of data from relational databases into Hadoop, Sqoop is a simple and economical mechanism with basic data ingestion functionality. Sqoop can load data directly into Apache Hive and can be integrated with Apache Oozie for scheduling.

While it can be helpful for initial development of a Hadoop solution, Sqoop has its drawbacks. Many administrators find data ingestion with Apache Sqoop to be cumbersome and quickly run into limitations on performance as their Hadoop data lake grows. Sqoop uses the MapReduce algorithm for data loading, which can increase the load on the Hadoop cluster as more concurrent queries are processed. And Sqoop's appeal as a free open source solution is mitigated by its difficulty to administer, optimize and monitor due to its dependence on manual scripting.

For data administrators seeking Apache Sqoop alternatives for superior Hadoop data ingestion and updates, Qlik (Attunity) provides a leading solution.

Qlik (Attunity): a data integration platform and Sqoop alternative

Qlik (Attunity) enables organizations to accelerate data replication, ingestion and streaming across heterogeneous databases, data warehouses and Big Data platforms. As a leading data management company, Qlik (Attunity) also addresses needs of modern databases, data warehouses, SAP, and real-time messaging system such as Kafka. Qlik (Attunity) enables easy data migration to cloud data warehouses, and provides enterprises with the tools to increase agility, optimizing analytics-ready data, while reducing dependence on developers.

Qlik (Attunity) vs. Sqoop

In contrast to Sqoop, Qlik (Attunity) enables data administrators to:

  • Create real-time data pipelines from producer systems into Hadoop with a graphical user interface that eliminates the need for manual coding or scripting. Automation features enable enterprises to initiate data lakes more quickly and to integrate additional sources as requirements change.
  • Ingest data into Hadoop from all major database and data warehouse platforms as well as a broad variety of other source systems.
  • Monitor Hadoop data ingest flows through a single console.
  • Use agentless change data capture technology to enhance real-time data pipelines without adversely impacting source database performance.
  • Ensure transactional consistency in Hadoop targets such as Hive data stores, partitioning updates by time to ensure each transaction is processed holistically to eliminate the risk of partial or competing entries.
  • Enjoy high performance and low risk, with certified integration into all Hadoop distributions including Cloudera, Hortonworks, MapR, and Kafka and Cloud platforms.

Streaming Change Data Capture