Apache Sqoop vs. Azure Data Lake Storage

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Sqoop
Score 8.8 out of 10
N/A
Apache Sqoop is a tool for use with Hadoop, used to transfer data between Apache Hadoop and other, structured data stores.N/A
Azure Data Lake Storage
Score 8.5 out of 10
N/A
Azure Data Lake Storage Gen2 is a highly scalable and cost-effective data lake solution for big data analytics. It combines the power of a high-performance file system with massive scale and economy to help you speed your time to insight. Data Lake Storage Gen2 extends Azure Blob Storage capabilities and is optimized for analytics workloads.N/A
Pricing
Apache SqoopAzure Data Lake Storage
Editions & Modules
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No answers on this topic
Offerings
Pricing Offerings
Apache SqoopAzure Data Lake Storage
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache SqoopAzure Data Lake Storage
Top Pros
Top Cons
Best Alternatives
Apache SqoopAzure Data Lake Storage
Small Businesses

No answers on this topic

Amazon S3 Glacier
Amazon S3 Glacier
Score 8.7 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Azure Blob Storage
Azure Blob Storage
Score 8.8 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.1 out of 10
Azure Blob Storage
Azure Blob Storage
Score 8.8 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SqoopAzure Data Lake Storage
Likelihood to Recommend
9.0
(1 ratings)
8.1
(15 ratings)
User Testimonials
Apache SqoopAzure Data Lake Storage
Likelihood to Recommend
Apache
Sqoop is great for sending data between a JDBC compliant database and a Hadoop environment. Sqoop is built for those who need a few simple CLI options to import a selection of database tables into Hadoop, do large dataset analysis that could not commonly be done with that database system due to resource constraints, then export the results back into that database (or another). Sqoop falls short when there needs to be some extra, customized processing between database extract, and Hadoop loading, in which case Apache Spark's JDBC utilities might be preferred
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Microsoft
Azure Data Lake is an absolutely essential piece of a modern data and analytics platform. Over the past 2 years, our usage of Azure Data Lake as a reporting source has continued to grow and far exceeds more traditional sources like MS SQL, Oracle, etc.
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Pros
Apache
  • Provides generalized JDBC extensions to migrate data between most database systems
  • Generates Java classes upon reading database records for use in other code utilizing Hadoop's client libraries
  • Allows for both import and export features
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Microsoft
  • Setting up Azure Data Lake Storage account, container is quite easy
  • Access from anywhere and easy maintenance
  • Integration with Azure Data Factory service for end to end pipeline is pretty easy
  • Can store Any form of data (Structured, Unstructured, Semi) in faster manner
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Cons
Apache
  • Sqoop2 development seems to have stalled. I have set it up outside of a Cloudera CDH installation, and I actually prefer it's "Sqoop Server" model better than just the CLI client version that is Sqoop1. This works especially well in a microservices environment, where there would be only one place to maintain the JDBC drivers to use for Sqoop.
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Microsoft
  • study for the certifications also to have them as a reference for work when you have any questions about applying a configuration to the equipment.
  • The Internet interface is simple and easy to use. Capacity is good and it's good that HP continues to innovate with this technology
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Alternatives Considered
Apache
  • Sqoop comes preinstalled on the major Hadoop vendor distributions as the recommended product to import data from relational databases. The ability to extend it with additional JDBC drivers makes it very flexible for the environment it is installed within.
  • Spark also has a useful JDBC reader, and can manipulate data in more ways than Sqoop, and also upload to many other systems than just Hadoop.
  • Kafka Connect JDBC is more for streaming database updates using tools such as Oracle GoldenGate or Debezium.
  • Streamsets and Apache NiFi both provide a more "flow based programming" approach to graphically laying out connectors between various systems, including JDBC and Hadoop.
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Microsoft
Azure Data Lake Storage from a functionality perspective is a much easier solution to work with. It's implementation from Amazon EMR went smooth, and continued usage is definitely better. However, Amazon EMR was significantly cheaper overall between the high transaction fees and cost of storage due to growth. The two both have their advantages and disadvantages, but the functionality of Azure Data Lake Storage outweighed it's cost
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Return on Investment
Apache
  • When combined with Cloudera's HUE, it can enable non-technical users to easily import relational data into Hadoop.
  • Being able to manipulate large datasets in Hadoop, and them load them into a type of "materialized view" in an external database system has yielded great insights into the Hadoop datalake without continuously running large batch jobs.
  • Sqoop isn't very user-friendly for those uncomfortable with a CLI.
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Microsoft
  • Instead of having separate pools of storage for data we are now operating on a single layer platform which has cut down on time spent on maintaining those separate pools.
  • We have had more of an ROI with the scalability as we are able to control costs of storage when need be.
  • We are able to operate in a more streamlined approach as we are able to stay within the Azure suite of products and integrate seamlessly with the rest of the applications in our cloud-based infrastructure
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