As more businesses realize the importance of end-to-end Business Intelligence (BI) solutions, demand for data engineers has risen significantly. Data engineers are responsible for extracting, cleaning, and normalizing data, while building data pipelines that data scientists can utilize to explore and build models. They are the backbones of data algorithm development and infrastructure design.
To succeed in their work, data engineers require a variety of data management tools, data warehouses, programming languages, and a host of other tools for data analytics, data processing, and AI/ML. This article discusses essential tools that data engineers require to create effective, efficient data infrastructure.
1. Amazon Redshift
Amazon Redshift is an excellent fully-managed cloud-based data warehouse powered by Amazon. It’s the optimal choice when it comes to choosing a solution to warehouse your data. Your data should be easy to access, well-sorted, and easy to manipulate and store to get maximum value from it, and Amazon Redshift offers you just that. Features that make Amazon Redshift an excellent data warehouse solution include:
- Ease of use
- It enables fast scaling with few or no complications
- It’s cost-effective
- It provides robust security tools
2. Databand platform
Databand is an excellent data observability platform for data engineers. It monitors what is happening in a data pipeline, allowing you to create reliable analytics, which help you produce trusted data products. It offers insights that monitoring tools cannot. Apart from telling you what went wrong, data observability platforms also reveal the cause of the problem and recommend actions to fix the problem.
3. Apache Spark
Companies today understand how crucial it is to capture data and make it available within the organization quickly. Stream processing allows you to process data as it is being produced or received, and Apache Spark is one such implementation of stream processing. It is an open-source analytics platform for big data processing and supports different programming languages, including Python, R, Scala, and Java.
4. Apache Airflow
Automating some tasks plays a key role in any sector and is an excellent way to reach functional efficiency. Without automating some tasks, you end up repeating the same task several times. As a data engineer, you have to deal with workflows such as collecting data from several databases, processing, cleaning, uploading, and reporting it. Consequently, it would be great if you automated some of these tasks.
Apache Airflow is one such tool that can help you schedule tasks, automate repetitive tasks, and streamline workflows. It makes running complex data pipelines easy. Apache Airflow is easy to use and has a great user interface that allows you to monitor progress and troubleshoot problems when required.
5. Snowflake
Snowflake is another excellent data warehouse with unbeatable data sharing capabilities and architecture. It offers the concurrency, elasticity, performance, and scale that today’s businesses need. It can easily ingest, transform and deliver data for deeper insights, helping to streamline data engineering activities. Among the unique benefits of this virtual data warehouse include:
- Ease of use – Snowflake has a simple and intuitive interface
- Fully automated – With snowflake, you don’t have to worry about updates, configuration, scaling your infrastructure, or failure
- Great tools like Mode Analytics, Tableau, Looker, and Power BI, which allows you to query data against large datasets
- Cost-effective
- Flexibility
- Robust security
6. SQL
Structured Query Language (SQL) is one of the key tools that data engineers need to build logic business models, extract key performance metrics, execute complex queries, and create reusable data structures. Additionally, SQL is one of the key tools that help access, insert, update, modify and manipulate data using data transformation techniques, queries, and more.
7. PostgreSQL
One of the most popular open-source relational databases, PostgreSQL, is a crucial tool for data engineers. It’s designed to work with large datasets, which makes it appropriate for data engineers. It is also popular with data engineers because of its extensibility and flexibility.
8. Tableau
Tableau is probably the most popular data visualization tool for business intelligence. You can use it to shape your output in the form of interactive charts and graphs. It also provides great visuals, and even a person with no knowledge about graphic design can create some incredible interactive charts and graphs. Tableau is mobile-friendly, and you can use it on your mobile device.
9. Power BI
Power BI is an excellent business intelligence tool by Microsoft. It’s an open-source cloud-based platform with a simple interface that allows users to create their own dashboards and reports.
Endnote
These are some of the top tools that data engineers can leverage to make data more useful to businesses.
Source: analyticsinsight.net
