Data Science



Top 13 Essential Tools for Data Engineering

Tools for Data Engineering

Organizations have tried to hire many Data and Machine Learning Scientists over the past decade to make better decisions and build data-driven products. 

Many of these companies failed to implement this strategy due to poor data quality, which led to weak predictive models. In some cases, the data is not even optimized for analysis because it is scattered.

A scalable architecture, automation, and best practices to work with data pipelines would make implementing and deploying models to production impossible, even when they performed well. Data Engineering plays a crucial role here.

By configuring the necessary processes, engineers can gather data from various sources using the techniques and principles of Data Engineering. The Data Engineers are also responsible for eliminating corrupted data so business users can access accurate and clean data.

Data Engineers, on the other hand, are among the most valued individuals in modern data-driven companies, as they are responsible for developing and improving products using the organization’s most valuable asset, the numerical data.

This can only be accomplished by using the right tools for data engineering to ultimately allow Data Engineers to build the tools that the organization needs.

Apache Hive

Apache Hive is a tool for storing and managing data in Hadoop. Through a SQL-like framework and interface, it processes data and extracts analytics.

The latest HDP 3.0 release of Apache Hive 3 includes new features that can optimize query performance while conforming to standards.

It can help manage workloads to meet demands, manage resources, and avoid resource conflicts with workload management.

Pros: 

  • You can access Hive 3 data from Apache Spark and Apache Kafka apps without restrictions 
  • Spark can read and write Hive databases using the Hive Warehouse Connector

Cons:

  • As a limitation of Hive-QL (HQL), Hive-QL cannot express iterative algorithms since it is challenging to encapsulate specific complex logical algorithms
  • Many OLTP features aren’t available in Hive, such as real-time queries and row-level modifications

Apache Spark

More than 52K customers are using Apache Spark for data analytics, including Apple, Microsoft, IBM, and others. In terms of data management and stream processing, it’s one of the fastest platforms available.

Uses Spark Streaming for real-time data analysis and changes to data stored in Hadoop clusters. It is 100 times faster to run Spark apps in memory and ten times faster to run Spark apps in a Hadoop cluster.

Pros

  • Iterative computations are well supported in Spark because of their computational design
  • Apache Spark supports graph processing through GraphX

Cons

  • Spark does not provide a file management system as an additional feature
  • The alternative will be to use another file management system

Apache Kafka

Among big data professionals, Kafka is viewed as a leading engineering tool by 907 contributors and 22k stars on Github. Data engineers use Kafka to create real-time streaming data pipelines.

Data is transmitted and received in real time by Kafka. An essential feature of Kafka is its adequate fault tolerance.

Pros

  • Due to its low latency, Kafka can handle large volumes of high-velocity events

Cons

  • Latency and missing files can occur due to the inefficient deployment of Kafka brokers

Apache Airflow

It makes managing, scheduling, and building data pipelines easier for data engineers, with over 8 million monthly downloads and 26K Github stars.

Airflow allows users to set up granular workflows and track their progress continuously. Multiple jobs can be managed simultaneously in this way.

Pros

  • Airflow offers a wide range of external system connectors
  • Support from a large community- Apache Airflow has around 500 active members who contribute to the project

Cons

  • Beginners must spend a lot of time with Airflow to understand the internal dynamics and even create custom modules to extend its capabilities

Tableau

Tableau is a popular tool for data engineering in the big data industry. Using Tableau’s drag-and-drop interface, data engineers can build dashboards to visualize data collected from multiple sources.

One of Tableau’s main selling points is its ability to handle large datasets easily. Dashboards can be generated without affecting the speed or performance of big data visualizations.

Pros

  • In addition to being highly efficient, Tableau quickly creates visually appealing dashboards
  • Tableau offers various visualization techniques for a better user experience
  • The tool is easy-to-understand and provides a smooth user experience

Cons

  • Automated data updating is not possible with Tableau
  • Tableau’s main disadvantage is its high price

Snowflake Data Warehouse

Snowflake provides data analytics and storage services via the cloud. A shared data architecture makes Snowflake an excellent tool for data scientists and engineers who want to migrate to a cloud-based solution quickly.

Many virtual warehouses can be created, each using its database for data processing. The ability of Snowflake to integrate semistructured data without using other tools, such as Hadoop or Hive, is one of the most significant innovations toward big data.

Pros

  • With Snowflake, you can secure your data with 256-bit AES encryption, IP allows, block lists, and multifactor authentication

Cons

  • It may seem that having no data restrictions for storage and computation is a great benefit
  • The Snowflake platform does not integrate with Amazon, Google, or Microsoft public clouds, although it can run on their services

Amazon Athena

In Amazon Simple Storage Service (Amazon S3), AWS Athena can be used to perform structured query analysis using Structured Query Language (SQL). Because AWS Athena is serverless, no infrastructure is required.

It allows you to access control lists, AWS Identity and Access Management (IAM) regulations, and guidelines for Amazon S3 buckets all enhance data security.

As Athena’s architecture can be extended in various ways, it is easy to work with any technology or tool.

Pros

  • Business owners can save money by using Amazon Athena by only paying for the queries they perform
  • Easily accessible since it runs queries using standard SQL, AWS Athena is widely accessible

Cons

  • Data optimization is impossible in AWS Athena; only query optimization is possible
  • Query resources are shared by all Amazon Web Services Athena users worldwide, according to the Service Level Agreement (SLA)

Power BI

As one of the leading tools for business intelligence and data visualization, Microsoft Power BI has captured more than 36% of the BI market share since 2021. With Power BI, data engineers can create live dashboards and analyze insights based on data sets.

One of Power BI’s most appealing features is its data analysis and visualization cost-effectiveness. Reports and dashboards can be created on your PC using Power BI’s free, basic desktop version.

Pros

  • With Power BI, you can read data from various sources, including Excel files and text files such as XMLs and JSONs
  • As well as BI tools such as Google Analytics, Facebook, and Salesforce can also gather data from these tools

Cons

  • It is limited in terms of customization options- Despite Power BI’s appealing visuals, it offers just a few customization options
  • Custom graphic approaches based on code cannot resolve this problem completely

Amazon Redshift

Redshift is Amazon’s cloud data management and warehousing platform, serving over 10K organizations worldwide. Among its many features is the ability to gather datasets, search trends, and anomalies, and generate insights. 

Users can manage millions of rows simultaneously using Amazon Redshift’s parallel processing and compression features.

It has Massive Parallel Processing (MPP) that can divide and conquer large data workloads across many processors. The processors use parallel processing rather than sequential processing.

Using column-oriented databases is significantly faster when retrieving large amounts of data.

Pros

  • Easily deployable – Amazon Redshift automates several administrative tasks, such as replication and backup, which makes it one of the simplest data warehouse technologies available today

Cons

  • The Parallel Upload feature in Redshift is only available for DynamoDB, SE, and Amazon EMR data sources.

Azure Databricks

Many organizations use Spark-based Azure Databricks for their data science and engineering teams. An analytics engine based on Spark is worth more than $38 billion. With the managed service, analysts, engineers, and scientists can access all the infrastructure and support for the latest analytics.

An interactive workspace is one of data engineers’ primary uses of Azure Databricks. In Azure Databricks, you pay only for active clusters, thanks to Databricks’ managed infrastructure. Saving money is made easy with many built-in features.

Pros

  • Platform with high performance and cost-effectiveness – Azure Databricks is a favorite among data engineers

Cons

  • Using Azure Databricks properly requires an understanding of multiple Azure data services. It may be challenging to understand the service documentation if you are a beginner
  • Visualizations, dashboards, and graphs are substandard,

PostgreSQL

Since PostgreSQL is fast, secure, and robust, it’s perfect for 99% of applications, so it’s an excellent place to start. There may be some other magic in other systems that you need. 

You probably already have everything you need with PostgreSQL, the “World’s most advanced open source database.”

Pros

  • The software runs on platforms with stable performance and works well with external data sources
  • The privacy and security of the client’s personal information are assured
  • There are many free forums where setup and usage are discussed

Cons

  • In PostgreSQL, all replicas can accept operations, which increases horizontal scaling
  • There is no need to reorder columns or improve data compression

MongoDB

New developers like MongoDB because it is flexible and easy to use. The system is straightforward to use but can meet all the complex requirements of modern applications. 

MongoDB stores all its documents in JSON, which is why many developers like it.

Pros

  • Data platform for developers built on the cloud
  • Schemas for documents that are flexible
  • Code-native data access with comprehensive support
  • Design that is change-friendly
  • The ability to query and robustly analyze data
  • Scaling out horizontally is easy with sharding

Cons

  • MongoDB typically has larger data sizes because it stores field names with each document, for example
  • Querying is less flexible (no JOINs), and transactions are not supported; atomic operations are supported only on a single record

Python

Python for data engineering is a universal language used for various purposes. A large and helpful community exists because of its popularity. 

Several big companies, such as Google, Amazon, and Facebook, also support Python. Computer programming languages like Python help build websites, automate tasks, and analyze data. 

Pros

  • Learning and reading Python is easy
  • The productivity of Python is enhanced
  • There are many libraries available for Python for data engineering
  • The Python community is vibrant, accessible, and open source
  • The Python programming language is portable

Cons

  • There are speed limits
  • Mobile computing relies heavily on computers and browsers
  • Limitations of design
  • Access layers to databases are underdeveloped
  • It’s simple

Why should you learn to use these tools?

Data engineering tools facilitate data transformation, which is a reason to use them. Considering that big data can take any form, whether structured or unstructured.

 As a result, data engineers require tools for transforming and processing big data. Using data engineering tools, data scientists can prepare data for analysis. Using these tools, you can integrate data sources and transform them to analyze data.

This tool enables data scientists to analyze large datasets more efficiently, bringing order to chaos. Open-source tools are available for download on Github.

Hero Vired’s Certificate Program in Data Engineering trains you to solve big data problems using data engineering tools in the data engineering training program. The data engineering training program is taught by a team of experts from the industry. 

It is a hands-on training program associated with placement assurance. It is suitable for people with a Bachelor’s degree and knowledge of python programming for data engineering.

It includes a capstone project to work on real-world problems using data engineering and big data tools. Data engineering is a vital skill to master, and the Hero Vired program, with its capstone project, will help you do that.

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