Sqoop vs. Flume Battle of the Hadoop ETL tools

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment. Data ingestion is important in any big data project because the volume of data is generally in petabytes or exabytes. Hadoop Sqoop and Hadoop Flume are the two tools in Hadoop which is used to gather data from different sources and load them into HDFS. Sqoop in Hadoop is mostly used to extract structured data from databases like Teradata, Oracle, etc., and Flume in Hadoop is used to sources data which is stored in various sources like and deals mostly with unstructured data.

Big data systems are popular for processing huge amounts of unstructured data from multiple data sources. The complexity of the big data system increases with each data source. Most of the business domains have different data types like marketing genes in healthcare, audio and video systems, telecom CDR, and social media. All these have diverse data sources and data from these sources is consistently produced on large scale.

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The challenge is to leverage the resources available and manage the consistency of data. Data ingestion is complex in hadoop because processing is done in batch, stream or in real time which increases the management and complexity of data. Some of the common challenges with data ingestion in Hadoop are parallel processing, data quality, machine data on a higher scale of several gigabytes per minute, multiple source ingestion, real-time ingestion and scalability. Apache Sqoop and Apache Flume are two popular open source etl tools for hadoop that help organizations overcome the challenges encountered in data ingestion. If you are looking to find the answer to the question -"What's the difference between Flume and Sqoop?" then you are on the right page. The major difference between Sqoop and Flume is that Sqoop is used for loading data from relational databases into HDFS while Flume is used to capture a stream of moving data.

Hadoop ETL tools:  

 ETL tools are used to move data between different systems. Data is said to be collected from multiple sources and represented in a destination in a different manner or in a different context than the data in the sources. For example, customer data is important for companies to track orders and ensure that their customers receive these orders. This same customer data is also used for further analysis and processing to identify buying patterns in the customers so that companies can handle their inventory accordingly. The data is essentially the same in both cases, but it is used to serve different purposes. In such cases, the data is copied into different systems to fulfill each purpose. 

The Hadoop ecosystem provides a variety of open-source technologies tailored for the purpose of ETL. They enable the connection of various data sources to the Hadoop environment. The data sources can refer to databases, machine data, web APIs, relational databases, flat files, log files, and RSS (RDF Site Summary) feeds, to name a few. Some of the ETL tools provided by Hadoop are:

• Apache Flume

• Apache Sqoop

• Apache HBase

• Apache Hive

• Apache Oozie

• Apache Phoenix

• Apache Pig

• Apache ZooKeeper

The selection of an ETL tool has to be determined considering several factors, including the amount of data, the rate of new data generation, the rate at which the data has to be processed, the source from which the data is to be collected and the type of data involved. The aim of selecting an ETL tool is to ensure that data is moving into Hadoop at a frequency that can meet the analytic requirements.

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Sqoop vs Flume-Comparison of the two Best Data Ingestion Tools

What is Sqoop in Hadoop?

Apache Sqoop (SQL-to-Hadoop) is a lifesaver for anyone who is experiencing difficulties in moving data from the data warehouse into the Hadoop environment. Apache Sqoop is an effective hadoop tool used for importing data from RDBMS’s like MySQL, Oracle, etc. into HBase, Hive or HDFS. Sqoop hadoop can also be used for exporting data from HDFS into RDBMS. Apache Sqoop is a command line interpreter i.e. the Sqoop commands are executed one at a time by the interpreter.

Need for Apache Sqoop

With increasing number of business organizations adopting Hadoop to analyse huge amounts of structured or unstructured data, there is a need for them to transfer petabytes or exabytes of data between their existing relational databases, data sources, data warehouses and the Hadoop environment. Accessing huge amounts of unstructured data directly from MapReduce applications running on large Hadoop clusters or loading it from production systems is a complex task because data transfer using scripts is often not effective and time consuming.

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How Apache Sqoop works?

Sqoop is an effective hadoop tool for non-programmers which functions by looking at the databases that need to be imported and choosing a relevant import function for the source data. Once the input is recognized by Sqoop hadoop, the metadata for the table is read and a class definition is created for the input requirements. Hadoop Sqoop can be forced to function selectively by just getting the columns needed before input instead of importing the entire input and looking for the data in it. This saves considerable amount of time. In reality, the import from the database to HDFS is accomplished by a MapReduce job that is created in the background by Apache Sqoop.

Features of Apache Sqoop

• Apache Sqoop supports bulk import i.e. it can import the complete database or individual tables into HDFS. The files will be stored in the HDFS file system and the data in built-in directories.
• Sqoop parallelizes data transfer for optimal system utilization and fast performance.
• Apache Sqoop provides direct input i.e. it can map relational databases and import directly into HBase and Hive.
• Sqoop makes data analysis efficient.
• Sqoop helps in mitigating the excessive loads to external systems.
• Sqoop provides data interaction programmatically by generating Java classes.

Companies Using Apache Sqoop

• The Apollo Group education company uses Sqoop to extract data from external databases and inject results of Hadoop jobs back into the RDBMS’s.
• Coupons.com uses Sqoop tool for data transfer between its IBM Netezza data warehouse and the hadoop environment.

What is Flume in Hadoop?

Apache Flume is service designed for streaming logs into Hadoop environment. Flume is a distributed and reliable service for collecting and aggregating huge amounts of log data. With a simple and easy to use architecture based on streaming data flows, it also has tunable reliability mechanisms and several recovery and failover mechanisms.

Need for Flume

Logs are usually a source of stress and argument in most of the big data companies. Logs are one of the most painful resources to manage for the operations team as they take up huge amount of space. Logs are rarely present at places on the disk where someone in the company can make effective use of them or hadoop developers can access them. Many big data companies wind up building tools and processes to collect logs from application servers, transfer them to some repository so that they can control the lifecycle without consuming unnecessary disk space.

This frustrates developers as the logs are often not present at the location where they can view them easily, they have limited number of tools available for processing logs and have confined capabilities in intelligently managing the lifecycle. Apache Flume is designed to address the difficulties of both operations group and developers by providing them an easy to use tool that can push logs from bunch of applications servers to various repositories via a highly configurable agent.

How Apache Flume works?

Flume has a simple event driven pipeline architecture with 3 important roles-Source, Channel and Sink.

• Source defines where the data is coming from, for instance a message queue or a file.
• Sinks defined the destination of the data pipelined from various sources.
• Channels are pipes which establish connect between sources and sinks.

Apache flume works on two important concepts-

1. The master acts like a reliable configuration service which is used by nodes for retrieving their configuration.
2. If the configuration for a particular node changes on the master then it will dynamically be updated by the master.

Node is generally an event pipe in Hadoop Flume which reads from the source and writes to the Sink. The characteristics and role of a flume node is determine by the behaviour of source and sinks. Apache Flume is built with several source and sink options but if none of them fits in your requirements then developers can write their own. A flume node can also be configured with the help of a sink decorator which can interpret the event and transforms it as it passes through. With all these basic primitives, developers can create different topologies to collect data on any application server and direct it to any log repository.

Features of Apache Flume

• Flume is a flexible tool as it allows to scale in environments with as low as five machines to as high as several thousands of machines.
• Apache Flume provides high throughput and low latency.
• Apache Flume has a declarative configuration but provides ease of extensibility.
• Flume in Hadoop is fault tolerant, linearly scalable and stream oriented.

Companies Using Apache Flume

• Goibibo uses Hadoop flume to transfer logs from the production systems into HDFS.
• Mozilla uses flume Hadoop for the BuildBot project along with Elastic Search.
• Capillary technologies uses Flume for aggregating logs from 25 machines in production.

Difference between Sqoop and Flume

• Apache Sqoop and Apache Flume work with various kinds of data sources. Flume functions well in streaming data sources which are generated continuously in hadoop environment such as log files from multiple servers whereas Apache Sqoop is designed to work well with any kind of relational database system that has JDBC connectivity. Sqoop can also import data from NoSQL databases like MongoDB or Cassandra and also allows direct data transfer or Hive or HDFS. For transferring data to Hive using Apache Sqoop tool, a table has to be created for which the schema is taken from the database itself.
• In Apache Flume data loading is event driven whereas in Apache Sqoop data load is not driven by events.
• Flume is a better choice when moving bulk streaming data from various sources like JMS or Spooling directory whereas Sqoop is an ideal fit if the data is sitting in databases like Teradata, Oracle, MySQL Server, Postgres or any other JDBC compatible database then it is best to use Apache Sqoop.
• In Apache Flume, data flows to HDFS through multiple channels whereas in Apache Sqoop HDFS is the destination for importing data.
• Apache Flume has agent based architecture i.e. the code written in flume is known as agent which is responsible for fetching data whereas in Apache Sqoop the architecture is based on connectors. The connectors in Sqoop know how to connect with the various data sources and fetch data accordingly.
• Lastly, Sqoop and Flume cannot be used achieve the same tasks as they are developed specifically to serve different purposes. Apache Flume agents are designed to fetch streaming data like tweets from Twitter or log file from the web server whereas Sqoop connectors are designed to work only with structured data sources and fetch data from them.
• Apache Sqoop is mainly used for parallel data transfers, for data imports as it copies data quickly where Apache Flume is used for collecting and aggregating data because of its distributed, reliable nature and highly available backup routes.

Sqoop vs Flume - Architecture

Apache Sqoop follows a connector-based architecture. This means that Sqoop has plugins that enable connectivity to external data sources. So, Sqoop can be used to bring data in from external sources, non-Hadoop stores into the Hadoop ecosystem. Sqoop is primarily used for parallel data transfer and hence, it is mainly used for cases where quick data transfer is required. Sqoop provides import tools and export tools to import tables from an external source into the Hadoop environment and export directories from the Hadoop environment into an external non-Hadoop database table, respectively. In Sqoop, the import or export processes terminate once the data transfer is complete.

Apache Flume follows an agent-based architecture, and is completely event-driven. An agent is an independent process in Flume, which receives data from clients or other agents. The agent then forwards the data to its next destination. There may be more than one agent in Flume. A Flume agent has three parts: 

• Source: the component of the agent which receives the data.

• Channel: receives the event from the source and holds them until they are consumed by the sink. 

• Sink: stores the data into centralized stores on the Hadoop ecosystem such as HBase and HDFS. it is responsible for consuming the events from the channel and then delivering it to the destination.

Since Flume is purely event-driven, it is primarily used to pull data when companies want to use the data on logs and social media to find patterns, root causes or perform sentiment analysis.

Sqoop vs Flume - Use cases

Apache Sqoop is an open-source tool designed to efficiently transfer bulk data between Hadoop and various structured datastores. Sqoop allows bidirectional data transfer between It is used to import data from external data sources into the Hadoop ecosystem. The Hadoop ecosystem includes HDFS or other systems such as Hive and HBase. Sqoop may also be used to export data from the Hadoop environment into external data stores. External stores may refer to relational databases and enterprise data warehouses. Sqoop can be coupled with relational databases such as Oracle, MySQL, Netezza, Teradata, Postgres, and HSQLDB. 

Apache Flume is a reliable and distributed open-source tool that is used for efficient collection, aggregation, and transfer of large amounts of log data. It provides a flexible and straightforward way of handling streaming data flows. This data can be used for further analytical analysis. Apache Flume provides a robust and fault-tolerant system with several recovery mechanisms.

Loading large amounts of data into Hadoop from production systems or using map-reduce applications running on large clusters to access the data can be very time-consuming since data transfer using scripts is inefficient. 

HDFS is a good tool for storing large volumes of data, and it also provides a scalable environment for processing both structured and unstructured data. However, it is not very suitable for queries requiring low latency or interactive queries. 

Apache Sqoop allows data transfer between the Hadoop ecosystem and external structured data sources for fast performance and with optimal system resource utilization. Sqoop copies data quickly from external data sources into Hadoop, enabling more efficient data analysis and mitigating the load on external systems. Sqoop reads the tables of the databases row-by-row onto Hadoop. The output generated is a number of files that contain copies of the table to be imported. Since the import process is performed in parallel, multiple files are generated as output. The files may be in the form of delimited text files, binary Avro, or Sequence files which contain record data in a serialized format.

Apache Flume is very effective in cases that involve real-time event data processing. Flume is ideal for situations where data of high volume and high velocity has to be collected from a variety of sources and stored onto the Hadoop system.

Some use cases of Sqoop are:

• Data can be imported from relational databases onto Hadoop. Sqoop supports loading the entire database, incremental loading of the database, and loading only some tables. The specific rows and columns to be imported can also be specified. Any delimiters, escape characters to be used in the file-based representation of the data, along with the file format to be used, can be specified. The import process takes the database table as the input. 

• After the data that has been imported is manipulated, the data can be exported back onto the relational database. The export process will read data from the files on Hadoop in parallel, parse the data into records, and then insert the records as new rows into the target database tables, which can be used by external users or applications.

• Sqoop provides commands which allow inspection of the database from where the data has to be imported or exported. There are commands to list the available database schemas and the various tables within a schema.

• During the import process, a Java class is generated, which can encapsulate a row of the imported table. The source code of this Java class is provided and can be used for any further MapReduce processing of the data. It allows serialization and deserialization of data to and from the Sequence File format. It can also be used to parse the delimited-text form of records.

According to HG Insights, over 10,000 companies including Nike, Bank of America, JP Morgan Chase, M&T Bank and Comcast use Apache Sqoop for bidirectional transfer of data between Hadoop and RDBMS.

Some real-world applications of Flume include:

• Flume can be used to import and analyze large amounts of data that are generated in real-time by various social media platforms such as Twitter and Facebook, and also data generated from e-commerce sites such as Amazon and Flipkart.

• Flume provides support for:

  • Fan-out flows: Here, the data can be exported to multiple destinations. There are two modes of fan-out flows - replicating and multiplexing. In the case of replicating, the data is exported to all channels. In multiplexing, the data is exported to only certain channels based on information in the event header. 

  • Fan-in flows refer to the import of data from multiple sources through a single channel.

  • Multi-hop flows: The Flume data may travel through multiple agents before it reaches the final destination. 

  • Contextual routing: The route of data from source to destination can be specified.

• Apache Flume is a good tool to use while performing sentiment analysis to inject the required data for analysis into the Hadoop environment.

• Flume finds applications in fraud detection. E.g., Credit card fraud detection.

• Flume allows the collection of data in real-time and in batch mode from a wide range of sources.

• Data can be collected from multiple sources and transferred to multiple destinations using Flume.

• Flume is the tool used for log data transfer to HDFS. In cases where there are multiple web applications servers that are generating logs, and the logs have to be moved quickly onto HDFS,Flume can be used to ingest all the logs rapidly into Hadoop. Flume can be used for data masking and data filtering.

• In IoT applications, Flume can help in the aggregation of data generated by machines and sensors.

• Flume is used to capture streaming data on Hadoop.

Flume vs Sqoop - Data flow

Apache Sqoop works with various relational database management systems (RDBMS) that have basic JDBC (Java Database Connectivity). Sqoop does not support importing of data from non-RDBMS such as MongoDB and Cassandra. Sqoop is not event-driven. It allows bidirectional transfer of data between Hadoop and RDBMS.This data transfer to and from Hadoop and the RDBMS is carried out in a parallelized fashion. Hence, the output while data is imported from external databases is usually in multiple files.

Flume works well with data sources where data is generated continuously. It is meant for transferring data from streaming data sources such as logs, jms, directory and crash reports into Hadoop. The distributed nature of Flume makes it a good choice for collection and aggregation of data from multiple sources. Flume is completely event-driven.

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