Cloudera Data Ingestion: Processing Data with Hadoop

With the use of artificial intelligence and the Internet of Things becoming more and more of a necessity to remain competitive, the challenges of the big data era are only increasing. Organizations must account for a greater volume of data extracted from more diverse locations in order to develop insightful action based on these developments. Therefore, typical big data frameworks Apache Hadoop must rely on data ingestion solutions to deliver data in meaningful ways.

Using a data ingestion tool is one of the quickest, most reliable means of loading data into platforms like Hadoop. When data ingestion is supported by tools like Cloudera that are designed to extract, load, and transform data, organizations can focus less on data ingestion concerns and more on capitalizing on data-driven insights for concrete business value. 

Cloudera data ingestion: An overview

Data ingestion is the means by which data is moved from source systems to target systems in a reusable data pipeline. For data to work in the target systems, it needs to be changed into a format that’s compatible. Data ingestion initiates the data preparation stage, which is vital to actually using extracted data in business applications or for analytics.

There are a couple of key steps involved in the process of using dependable platforms like Cloudera for data ingestion in cloud and hybrid cloud environments. The process usually begins by moving data into Cloudera’s Distribution for Hadoop (CDH), which requires several different connectors for data integration and processing. 

The connectors are useful for both moving and transforming data from source systems to a number of tools that work within Hadoop’s ecosystem. This process typically requires a considerable amount of mapping, which is one of the challenges of data integration. Once this manual mapping is completed, however, it can be automated. Additionally, there’s a metadata layer that allows for easy management of data processing and transformation in Hadoop. 

How to find the best Cloudera data ingestion tools

In theory, there are several different tools that organizations can choose from for Cloudera data ingestion. Most of these tools, if not all, use ETL or ELT to move data into Hadoop in a useful format, automate the ingestion process, and are optimized to work in both cloud and hybrid cloud settings. In particular, an automated tool can be reused without manually writing code.

Since a Cloudera data ingestion tool is often responsible for the heavy lifting, it’s essential to choose the best one for your business. Credible Cloudera data ingestion tools specialize in: 

• Extraction: Extraction is the critical first step in any data ingestion process. It enables data to be removed from a source system and moved to a target system. The best Cloudera data ingestion tools are able to automate and repeat data extractions to simplify this part of the process. Data extraction usually begins by taking a small sample of the data to understand its schema, format, and data model. After data is extracted from a source system it usually requires some sort of transformation so it can conform to the target system’s data model.
• Visualization: The visualization capabilities of Cloudera data ingestion tools are extremely important. Top tools involve intuitive, graphic visualizations so users simply have to move objects on their screen to perform complicated data integrations. Although to the user it just looks like moving an icon from one place to another on the screen, there’s a lot of work involved in replicating and transforming datasets. All of this difficulty is masked by this visual approach to data ingestion, which effectively makes Cloudera data ingestion easy for business users.
• Scalability: Scalability is a fundamental requirement of tools for Cloudera data integration — data ingestion tools should be able to scale both horizontally and vertically. The former means the tool can scale to accommodate datasets in a broad range of data sources, and transform and move data into the Hadoop ecosystem. Scaling vertically, these tools should have the capability to handle immense amounts of data. These mechanisms should have the ability to scale up as well as scale down for both batch jobs and ad-hoc jobs for individual users.
• Easy integration: The best Cloudera data ingestion tools automate the mapping required for integrating diverse datasets with convenient connectors between some of the most popular sources and specific components in the Hadoop Distributed File System (HDFS). There are multiple factors to consider for integrating data, including data models, schemas, transformation, and loading data. Competitive tools are able to streamline all of these factors for painless integrations.
• Security: Security is a necessity for all things data related, especially data ingestion as  data must securely replicate from one location to another. In this process, it’s not enough to simply fortify the end points. Organizations must be sure that the data is protected while it’s in motion. Reliable data ingestion tools are able to provide this benefit in order to ensure all data is moved appropriately. 

Each of these considerations underscore the huge value that Cloudera data ingestion tools provide. The best ones include all of these advantages with the flexibility required for today’s distributed data landscape at the speed of business.

Understanding Cloudera data ingestion tools

There is an assortment of tools that are useful for Cloudera data ingestion once the data is moved within Hadoop. Each of the available options are helpful for unique use cases, such as for performing ETL or working with real-time streaming datasets. It’s critical to understand which one works best based on your business needs in order to optimize investments. 

• Apache Flume: The primary use case for Apache Flume is to transport log data from a host of distributed data sources to a centralized repository. Log data is useful for security and data governance purposes: it reveals who accessed which type of system for how long, and other factors useful for reinforcing data governance and security. Flume is also used to collect and aggregate data. 

Aggregating this data enables users to identify trends at a more macro level, which can impact different measures for data governance and security. Flume’s architecture is based on the flow of streaming data; its data model is relatively simple and works with online analytic applications.  

• Apache Kafka: Apache Kafka is well known for its distributed messaging that consistently delivers a high throughput. It functions as an extremely quick, reliable channel for streaming data. As such, it’s helpful for many different applications like messaging in IoT systems.

Another advantage of relying on Kafka for near real-time messaging is its capability of transmitting hundreds of thousands of messages each second that are persisted to disc and copied throughout specific clusters. Consequently, data loss is almost entirely eliminated. Kafka’s rapidity also enables messages to be delivered concurrently between a host of different parties which is ideal for multi-tenant deployments.

• Apache Sqoop: The main use case for Apache Sqoop is to move data from Hadoop to traditional relational environments, including SQL-based data warehouses. Sqoop is most efficient at issuing these data transfers in batch loads, although it is useful for smaller datasets as well. 

Sqoop is immensely beneficial in that it enables organizations to offload some of the tasks for data ingestions (like ETL) that are typically performed by their data warehouses to Hadoop. Doing so is a means of conserving resources, lowering costs, and increasing overall efficiency. This tool works with almost all the major relational data warehouse vendors. Since Sqoop is open source, its code is found on popular sites such as GitHub and Gitbox.

• Apache Hive: Apache Hive is one of the more well-known tools in the Hadoop ecosystem. Hive is a foundational tool to CDH and provides widespread utility by focusing on ETL. It’s important to understand Hive’s role within the overall pipeline for data ingestion. Once data has been moved within the Hadoop ecosystem, Hive is the means by which it can be extracted, transformed, and loaded into other applications or analytics for actionable insight. 

Hive’s ETL capabilities excel at joining different datasets or even transforming them for various Business Intelligence applications. It ingests data in a number of formats, the most flexible of which are Apache Parquet and Avro. Both of these formats support schema on demand.  

• Apache Impala: Apache Impala is designed to optimize the use of SQL queries within the Hadoop ecosystem. With Impala, SQL queries are both performant and extremely quick to support exploratory analytics on data — which is not typically possible running SQL queries in conventional relational settings that are optimized for batch jobs. The speed at which Impala facilitates interactive SQL queries in Hadoop corrects one of the principle limitations of using this query language with HDFS. 

Since Impala is another important component in CHD, it integrates well with virtually all the other tools found in this distribution. As such, it noticeably expands the utility of SQL to include big data settings, so organizations can still use some of the same approaches they’ve made substantial technology investments in over the years.

Getting started with Cloudera data ingestion

Cloudera data ingestion is an effective, efficient means of working with all of the tools in the Hadoop ecosystem. It enables organizations to realize the benefits of working with big data platforms in almost any environment — whether in the cloud, on-premises, or in a hybrid-cloud. It’s essential for consistently ingesting data for cutting edge applications of cognitive computing, IoT, and working with data at scale.

Still, Cloudera data ingestion works best with reliable tools for connecting data from source systems to target ones, including all of the required mapping and transformations. With its graphic visualizations, Talend lets organizations easily move data into CHD to leverage the variety of Hadoop tools critical to managing big data. Furthermore, it replicates this data in a secure, well governed manner with the necessary data lineage to support ongoing processes.

When accessed through Talend Data Fabric, organizations can act on Cloudera data ingestion in conjunction with the necessary enterprise-wide data integrations with speed. Talend Data Fabric handles all aspects of data ingestion, including transforming and loading data into source systems. Try Talend Data Fabric today to securely ingest data you can trust into your Hadoop ecosystem.