How to choose an appropriate big data tool

Selecting the right big data tool for a task depends on many questions, and this decision varies from task to task. In this Answer, we’ll provide a basic understanding of this crucial and attention-worthy decision.

Roadmap to decide on a big data tool

Here’s a series of questions we can use to answer to find a suitable tool for a particular task:

1. What is the type of big data task?

2. What is the nature of the data involved in the task?

3. What are the scaling requirements of the task?

4. What are the performance requirements of the task?

5. What are the desired levels of ease of use, flexibility, ecosystem, and integration of the tool?

6. What are the security requirements related to the involved data?

Let’s dive deep into the above questions and enlist recommended big data tools for each scenario.

Types of tasks

Big data tasks can vary based on the required action that needs to be performed on the data. A high-level division of tasks related to big data and the appropriate choice of big data tool for each type is as follows:

1. Data storage: Tools such as Apache Hadoop HDFS, Apache Cassandra, and Apache HBase disseminate enormous volumes of data.

2. Data processing: Tools such as Apache Hadoop MapReduce, Apache Spark, and Apache Storm distribute and handle enormous volumes of data.

3. Data ingestion: Tools such as Apache Nifi, Apache Kafka, and Apache Flume are used to collect and analyze data from diverse sources.

4. Data analysis: SQL-like languages are used to analyze data using tools such as Apache Hive, Apache Pig, and Apache Impala.

5. Data visualization: Data visualization tools such as Apache Superset, Tableau, and Grafana are used to visualize data and create interactive dashboards.

Nature of data

When selecting the tool to process big data, it’s crucial to consider the data type. Here are a few instances of how the nature of data might affect the tool selection:

1. Data format: Tools like Apache Hive or Apache Pig may be viable if the data is structured like CSV or JSON since they can handle structured data using SQL-like languages. Tools like Apache Mahout or TensorFlow may be more suited if the data is unstructured, such as text or photos, as they can handle unstructured data using machine learning methods.

2. Data volume: A distributed storage and processing system, such as Apache Hadoop or Apache Spark, may be required to handle the data volume if it is particularly vast. These tools have been created to scale horizontally over a machine cluster.

3. Data velocity: Tools like Apache Storm or Apache Kafka may be better if the data is created in real-time and has to be processed close to real-time. These tools are made for high throughput and real-time stream processing.

4. Data variety: Tools like Apache Nifi or Apache Kafka may gather and standardize the data before it is processed if it arrives from many sources and in various forms.

5. Data structure: Tools like Apache Pig, Hive, or Pig may extract valuable information from semi-structured or unstructured data.

Scaling requirements

Performance requirements

If the work demands quick data processing, an in-memory processing tool like Apache Spark may be a better solution than disk-based techniques like Apache Hadoop MapReduce.

Tool specifications

The choice of tool can also depend on the associated features of the tool.

• Ease of use: If the work requires minimum development time and a short learning curve, a tool with a SQL-like interface, such as Apache Hive or Pig, may be an excellent alternative.

• Flexibility: If the work necessitates modifying and expanding the tool to meet unique needs, a tool such as Apache Spark or Apache Storm, which give APIs for creating custom code, may be a viable solution.

• Ecosystem: If the work demands interaction with other systems and data sources, a tool with a rich ecosystem, such as Apache Hadoop or Apache Spark, may be a viable choice since it offers a wide range of libraries for data processing, storage, and analysis.

• Integration: If the work needs integration with other systems and data sources, such as data lakes, data warehouses, and machine learning platforms, a solution like Apache Nifi, Apache Kafka, or Apache Flume may be appropriate.

Data security requirements

If data security and privacy are a concern, tools like Apache Ranger and Apache Atlas, which enable fine-grained access control and data governance capabilities, may be a viable alternative.

Example

Let’s take an example of how we may assess a task’s unique requirements:

Implement a data processing pipeline for an e-commerce website to evaluate consumer activity and produce insights for sales growth.

1. Determine the task’s precise requirements and objectives:

   1. Gather information on customer activity, such as browsing history, purchase history, and demographics.

   2. Data should be processed to create insights such as customer segmentation, product recommendations, and sales patterns.

   3. Visualize the findings so that the company may make data-driven decisions.

2. Examine the data’s nature:

   1. The data is created in real-time and must be handled in near real-time.

   2. The majority of the data is organized and in JSON format.

   3. The information is derived from various sources, including website logs, order history, and client demographics.

3. Examine the available resources:

   1. There is a cluster of computers accessible with moderate CPU and memory resources.

   2. The team has prior knowledge of Apache Hadoop and Apache Spark.

   3. The team has worked with data visualization technologies such as Tableau.

4. Consider the task’s specific requirements:

   1. The data must be handled in real-time, necessitating a tool with high throughput, real-time stream processing capabilities, such as Apache Kafka or Apache Storm.

   2. Since the data is organized and in JSON format, it can be processed using a program like Apache Hive or Pig.

   3. The data is generated from numerous sources, so a tool such as Apache Nifi can be used to gather and standardize it.

   4. Since the team is familiar with Apache Hadoop and Apache Spark, these technologies may be utilized to handle data at scale.

   5. As the team is familiar with data visualization technologies such as Tableau, the insights may be displayed using these tools.

Recommended choices

Based on this evaluation, a data processing pipeline consisting of the following tools can serve the purpose right:

1. Use Apache Kafka for real-time data collection, ingestion, and stream processing.

2. Use Apache Hive or Pig for processing structured data.

3. Use Apache Hadoop or Apache Spark for large-scale data processing and storage.

4. Use Tableau for data visualization.