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.
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.
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:
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.
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.
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.
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.
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.
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.
Flume has a simple event driven pipeline architecture with 3 important roles-Source, Channel and Sink.
Apache flume works on two important concepts-
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.
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.
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 Hadoop and RDBMS. 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.
If you would like to learn more about how Apache Sqoop and Apache Flume can help you with your Hadoop data ingestion needs-Click Here.