List of Apache Sqoop Customers
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Since 2010, our global team of researchers has been studying Apache Sqoop customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Apache Sqoop for Data Migration from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Apache Sqoop for Data Migration include: Allstate, a United States based Insurance organisation with 55000 employees and revenues of $67.69 billion, NextEra Energy, a United States based Utilities organisation with 16800 employees and revenues of $24.75 billion, Freddie Mac, a United States based Banking and Financial Services organisation with 8004 employees and revenues of $21.20 billion and many others.
Contact us if you need a completed and verified list of companies using Apache Sqoop, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
The Apache Sqoop customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Allstate | Insurance | 55000 | $67.7B | United States | Apache Software | Apache Sqoop | Data Migration | 2017 | n/a |
In 2017, Allstate implemented Apache Sqoop to centralize ETL extraction into its Hadoop data lake supporting Electronic Underwriting and Marketing Lead Channel Routing use cases. Apache Sqoop was used to ingest batch and incremental datasets from the Allstate warehouse and third party sources such as ExamOne, HumanAPI, and Magnum, feeding downstream model input and analysis workflows in the data lake.
The implementation integrated Apache Sqoop with a broader pipeline stack including Scala, Spark, PySpark, Ruby and Rake scripts, UNIX orchestration, and Airflow and Tivoli schedulers for refresh processing. Data landing and analytical access were provisioned into Hadoop and AWS Scality S3 storage, with tables surfaced to Domino and Dremio for model development and to Tableau for data scientist visualization. Operational scope covered ETL extracts for marketing and underwriting analytics, and the implementation emphasized pipeline automation, table creation for model inputs, and scheduler-driven refresh governance.
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Freddie Mac | Banking and Financial Services | 8004 | $21.2B | United States | Apache Software | Apache Sqoop | Data Migration | 2021 | n/a |
In 2021, Freddie Mac implemented Apache Sqoop in a Data Integration deployment to support bulk data ingestion for analytics and machine learning. The implementation positioned Apache Sqoop as a connector-based ingestion layer extracting relational data from Teradata and web server sources into cloud-backed storage for downstream processing.
Apache Sqoop was configured for scheduled and ad hoc bulk transfers, using parameterized import jobs, column and table mappings, and data partitioning to optimize throughput. Job definitions and script-based orchestration were used to produce file formats consumable by Spark Streaming API and batch analytics, enabling repeatable ingestion patterns for the data science and engineering teams.
The deployment integrated Apache Sqoop with Teradata and with streaming and processing components including Kafka and Spark Streaming API, and it operated alongside AWS infrastructure components EC2, S3, and RDS. Operational ownership focused on data engineering and data science groups that used the ingested data to support mortgage risk modeling, supervised and unsupervised learning objectives, and advertisement recommendation pipelines.
Governance activities included pipeline orchestration, scheduling, monitoring, and logging to maintain data consistency and failure handling across imports. The Apache Sqoop implementation supported machine learning pipelines that the internal notes report improved mortgage approval decision efficiency by 14% and enhanced advertisement recommendation engagement, while EC2, S3, and RDS management supported operational performance and scalability.
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NextEra Energy | Utilities | 16800 | $24.8B | United States | Apache Software | Apache Sqoop | Data Migration | 2018 | n/a |
NextEra Energy implemented Apache Sqoop in 2018 as part of a cloud native analytics environment, deploying Apache Sqoop within a Data Integration pipeline to support bulk ingestion of IoT sensor data from on premises databases into Amazon S3 and downstream analytics platforms. The implementation was executed for the IT department and targeted Florida wide solar infrastructure data to support executive decision making on panel efficiency and maintenance planning.
The deployment integrated Apache Sqoop with an AWS based ingestion architecture, including AWS DMS, AWS Glue, Amazon S3, AWS Lambda, and Amazon Redshift. Apache Sqoop was configured for parallel imports and incremental load patterns to stage data into S3 and Redshift, while AWS Glue and Lambda handled transformation and orchestration. Infrastructure and environment provisioning were automated through AWS CloudFormation to ensure repeatable provisioning across environments.
Operational integrations included Redshift connectivity to Power BI via ODBC and JDBC connectors and Amazon QuickSight for hybrid business intelligence reporting. Security and governance were enforced through AWS IAM policies, VPC isolation, and KMS encryption, and observability was implemented with Amazon CloudWatch dashboards monitoring ETL job health, data latency, and Redshift query performance. The solution aligned with on premises network, data, and compliance stakeholders, served as a reference model for future departmental cloud initiatives, reduced setup time by 25 percent through automation, and improved issue detection and incident response by 40 percent as measured by internal monitoring.
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