Data Engineering Tools

October 14, 2022 Maitri Katti
Reading Time: 7 minutes

What is data Engineering?

Data engineers plan and construct pipelines that transform and ship data into a format that is highly usable when it is received by data scientists or other end users. These pipelines are required to gather data from various unrelated sources and combine it with data from other sources into a single warehouse that acts as a single source of truth for all of the data.
Data engineering makes data more usable and available for data consumers. Data engineering must collect, modify, and analyze data from each system to achieve this. Similar to an Excel spreadsheet, relational databases manage data as tables. There are numerous rows in each table, and each row has the same columns. Numerous tables may be used to store a single piece of data, like a customer order.

What are the Data Engineering  Responsibilities?

  • Data is organized through data engineering to make it simple for users and other systems to use. They collaborate with a variety of data consumers.
  • Data analysts answer specific questions about data or build reports and visualizations so that other people can understand the data more easily. Data scientists who answer more complex questions than data analysts do For example, a data scientist might build a model that predicts which customers are likely to purchase a specific item.
  • Designers of data systems who are in charge of integrating data into the applications they create. For instance, a systems architect might provide the infrastructure for an online retailer to give discounts based on a customer’s past purchases.
  • Identifying the data needs, such as how long the data must be retained for, its intended use, and the systems and individuals who require access to it.

Data Engineering Tools

1. Python: Data engineering is gradually becoming the backbone of companies looking forward to leveraging data to improve business processes. This blog will discover how Python has become an integral part of implementing data engineering methods by exploring how to use Python for data engineering. Python is a general-purpose programming language called Python is frequently employed in the creation of data engineering systems. It provides a range of functions and instruments for constructing data pipelines and automating processes.

Python programming is frequently used for data mugging activities, including reshaping and aggregating, in order to quickly and automatically execute data analysis. Pandas, NLTK, scikit-learn, matplotlib, and other Python libraries are ideal for carrying out a variety of data engineering and data science-related tasks. Based on an analysis of how frequently tutorials for different programming languages are searched on Google, Python tops the PYPL-Popularity of Programming Language Index.

2. Snowflake: A cloud-based data warehouse application is called Snowflake. For engineers, it offers tools for  computation, data storage, and cloning.It is a platform that was created from the ground up to support for data   science driven by machine learning and AI. Snowflake makes it simple to prepare data and create ML models. Customers don’t have to be concerned about difficult integrations or associated costs.

Modern Data Engineering with Snowflake’s platform allows you to use data pipelines to move data into your data lake or data warehouse. Data pipelines in Snowflake can be batch or continuous, and processing can happen directly within Snowflake itself. Snowflake works with a wide range of data integration tools, including Informatica, Talend, Fivetran, Matillion and others.

3. Big Query: Big Query is a cloud-based data warehousing tool that is completely managed. It allows engineers and analysts to enter data, process it, and modify the scope and time frame of activities to suit their evolving demands. Machine learning technologies, business intelligence analysis, and real-time data reporting are some of Big Query’s key capabilities.
By separating the computation engine that analyses your data from your storage options, Big Query enhances flexibility. BigQuery can be used to evaluate your data’s location or to store and analyze your data there. While streaming enables continuous data updates, federated queries allow you to read data from external sources. You can analyze and comprehend that data with the help of potent tools like Big Query ML and BI Engine.

4. Apache Hadoop: Hadoop is one of the most commonly used Big Data engineering tools. Apache Hadoop is a collection of open-source software utilities that facilitates using a network of many computers to solve problems involving massive amounts of data.
It is a framework that enables simple programming concepts to be used for the distributed processing of big data sets across clusters of computers. It is intended to scale up from a single server to thousands of devices, each providing local computing and storage. computation. It offers a software framework for the Map Reduce programming model-based distributed storage and processing of massive data.

5. Airflow: It is a platform for creating, planning, and managing processes programmatically. Data engineers utilize it as one of the most reliable ETL (Extract, Transform, Load) workflow management tools to orchestrate workflows or pipelines. You can see the dependencies, logs, code, trigger jobs, and progress status of your data pipelines with Airflow. The development of Apache Airflow started at Airbnb as an open-source project in 2014. Currently, It is currently a component of an Apache Software Foundation project that the general public can view on GitHub.

It is used by more than 200 companies, including Airbnb, Yahoo, PayPal, Intel, and others. You are invited to join the community as a developer, report and fix bugs, add new features, and improve the documentation.

Airflow consists of 2 Key Components:

  1. Scheduler: When all of the dependencies for all of the tasks and DAGs (Directed Acyclic Graph) have been satisfied, the task instances are triggered by the Airflow scheduler. The scheduler launches a sub process that keeps track of and syncs with each DAG in the designated DAG directory. The scheduler uses the configured Executor to run tasks that are ready.
  2. Executor: Executors are in charge of carrying out the tasks. They share an API, and depending on your installation requirements, you can switch out the executors. One executor can only be configured at a time for airflow. The [core] section of the configuration file’s executor option can be used to change this.For instance, merely state the executor’s name if you are using a built-in executor.

Conclusion:

Data analysis is extremely crucial. Data engineering has had a significant positive impact on businesses who earlier struggled to keep up with the enormous amounts of data they gather. Data scientists have the ability to provide priceless insights that have the potential to upend entire businesses through creative data engineering. Data engineering is a crucial component of almost all company goals. Data engineers prepare and process data for later analysis using a variety of specialized techniques and equipment. It goes without saying that data isn’t useful unless it is readable. Thus, data engineering is the first step in making data useful.

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Reading Time: 7 minutes

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