Data engineers convert unstructured data into valuable information. They create data collection, fusion, and transformation pipelines that assist businesses in performing seamless analytics. Perhaps, it won’t be wrong to say that they are in charge of designing an architecture that supports cutting-edge data analytics.

However, manual engineering and managing enormous datasets to produce sophisticated models is no longer an option as the number of large datasets grows, and applications get more complicated. It is important that data engineers’ needs are classified into distinct groups, and different tools are used to address these requirements. 

Let’s learn about the same in detail!

What Is Data Engineering?

The process of developing, managing, and maintaining software systems that gather, store, and analyze data for an organization is known as data engineering. These programs employ a wide range of internet resources, tools, languages, and software. 

With the help of effective data engineering, data scientists and analysts can make well-informed decisions. They can track and improve manufacturing, sales, distribution, and profit strategies, ensuring the company is going on the right track. 

Must-Have Data Engineering Tools For Your Team

Apparently, even the most talented companies providing services of Data Engineering in Latin America require specific tools and frameworks to reap the perks of first-party data. Moreover, there isn’t a tool that works for everyone, so it’s important to use one that aligns with your company’s objectives. 

That being said, let’s get well acquainted with the tools. 

Amazon Redshift

Redshift is a cloud-based data management and warehousing program that fully utilizes Amazon Web Services (AWS). Redshift is primarily an analytics platform that gathers and segments datasets, searches for trends and irregularities, and generates insights rather than engineering the data to develop new solutions.

Redshift makes it simple to use conventional SQL to query and aggregate massive amounts of structured and semi-structured data from data lakes, operational databases, and warehouses. Moreover, it speeds up the time it takes to gain information by enabling data engineers to quickly incorporate additional data sources.

Yes, it is true that Redshift requires some understanding, but it is well worth the effort because major brands and firms use it for their data. The easiest way to demonstrate your Redshift proficiency is to import a large amount of data, analyze it, and make use of the tool for information.

Google Cloud Platform (GCP)

Google Cloud Platform provides safe and flexible solutions to businesses. If we talk about cloud storage, in particular for pictures, documents, spreadsheets, multimedia, video, and even websites, you can definitely rely on Google Cloud Storage. The cost is determined by how much storage space you use, and you receive limitless access to it. 

It is really helpful for SMEs and startups, ensuring the safety of their confidential information, even if exchanged between different parties. The information is generally an immutable file and is kept in a storage space known as a bucket. The buckets are related to projects that can be grouped into organizations.

Apache Spark

Nowadays, businesses are aware of the value of gathering data and making it rapidly accessible throughout the company. Using stream processing, you may query ongoing data streams in real time, including information from sensors, website user activity, IoT device data, financial trade data, and much more. One such well-liked Stream Processing implementation is Apache Spark.

Apache Spark is an open-source analytics engine that supports a number of programming languages, such as Java, Scala, R, and Python. It is well known for its ability to analyze enormous amounts of data. Spark leverages in-memory caching and efficient query execution and is capable of processing terabytes of streams in small batches.

Apache Kafka

Apache Kafka is a tool that can assist you in developing a data pipeline capable of handling massive amounts of information. It is often used by big firms and financial institutions, but it also works quite well for smaller enterprises.

With Kafka, you can instantly consume and evaluate any kind of message. It has built-in high-availability features to ensure that your data is always readily available. It also keeps messages in subjects so they may be recovered later.

Apache Kafka is an open-source event Streaming solution that is easy, dependable, scalable, and high-performance, much like Apache Spark.

PostgreSQL

If you are looking for the world’s most widely used open-source relational database, PostgreSQL is where your search ends. The active open-source community of PostgreSQL (not a company-led open-source tool like DBMS or MySQL) is one of the many factors contributing to the database’s popularity.

PostgreSQL was created using an object-relational paradigm and is very light, flexible, and powerful. It offers a wide variety of pre-built and user-defined features, large data storage, and reliable data integrity. PostgreSQL is a great option for data engineering operations since it is specifically made to handle massive datasets and has strong fault tolerance.

Snowflake

Snowflake offers a warehouse-as-a-service solution to meet the needs of today’s businesses. It is truly credited with creating, perfecting, and resurrecting the data warehouse industry. It enables users to transition quickly to a cloud-based system. The three layers of Snowflake’s architecture are the Database Store, Query Processing, and Cloud Services.

Managed infrastructure, on-the-fly scalability, automatic clustering, and seamless integration with ODBC, JDBC, Javascript, Python, Spark, R, and Node.js are among its best features.

The revolutionary architecture of Snowflake fully utilizes the cloud and incorporates the advantages of both shared-disk and shared-nothing architecture. While processing queries using MPP (massively parallel processing) compute clusters, Snowflake’s central data repository has access to the information in compute nodes. A subset of the full data set is saved locally on each node. 

Microsoft Power BI

Microsoft Power BI, a renowned Business Intelligence and Data Visualization tool, is employed in analytical application cases to visualize data in a better business-friendly form by turning data sets into real-time dashboards and analysis insights. As a matter of fact, even non-technical users may easily produce reports and dashboards with the use of Power BI’s cloud-based services and simple user interface.

Power BI offers hybrid deployment ability, which is often employed while compiling information from several sources, including cloud-based and on-premises sources like SAP, SQL Server, Salesforce, Oracle Database, and MongoDB. In Simple terms, it provides reports that will assist your future business decisions. The following components are included in the Power BI application suite: 

• Power BI Desktop, 

• Power BI Service, 

• Power BI Report Server, 

• Power BI Marketplace, 

• PowerBi Mobile Apps, 

• Power BI Gateway, 

• Power BI Embedded, and 

• Power BI API.

Hadoop

Hadoop is a collection of open-source tools designed to manage large-scale data, which is frequently created by massive computer networks, as opposed to having a single tool with a constrained set of functions. Its capacity for real-time data processing, thorough and clear analytics and orderly data storage has made it a household brand for many organizations.

Although anyone with a foundation in SQL may easily get started with Hadoop because of its reliance on SQL for its databases, mastering the program would take a lot of time and effort. Hadoop won’t be going away anytime soon, particularly with well-known firms demonstrating why it’s a vital tool.

MongoDB

Other popular NoSQL databases include MongoDB. It can store and analyze both structured and unstructured information at a large scale and is very user-friendly and adaptable. Due to their capacity to manage unstructured data, NoSQL databases—including MongoDB—have grown in popularity. Contrary to relational databases (SQL), which have strict schemas, no-Sql databases are considerably more flexible and store data in simple, understandable formats.

MongoDB is a great option for processing large amounts of data since it has features including a distributed key-value store, document-oriented NoSQL abilities, and MapReduce calculating skills. Since data engineers frequently work with unprocessed, raw data, MongoDB is a well-known option that maintains data functionality while enabling horizontal Growth.

Python

Last but not least, data engineers use the programming language Python. It’s simple to learn, versatile, and is now accepted as the industry norm for data engineering.

Due to its many applications, particularly when creating data pipelines, Python is sometimes referred to as the Swiss army knife of programming languages. Python is used by data engineers to program ETL frameworks, API interfaces, automation, and data munging operations, including reshaping, aggregating, and merging different sources, among other things.

Other advantages of Python include an easy syntax and a plethora of third-party libraries. The main benefit of using this programming language is that it speeds up development, which lowers costs for businesses. Moreover, python has been regarded as a standard in the current data science environment to conduct complex statistical analyses, create data visualizations, develop machine learning algorithms, and perform other data-related tasks. 

To Sum It All Up

Truth be told, today’s data engineers have a ton of options; this list just includes the top 10 data engineering tools. Nevertheless, these tools are among the best and lifesaving for data engineers who want to create a reliable and effective data architecture.

An ETL/ELT solution is essential since the ultimate objective is to construct a strong and adaptable data analytics architecture that processes data methodically and can run for years with little maintenance.

Author Bio: Amelia Jones works with Outreach Monks as senior content head. She holds her expertise in business and technical writing. Her aim is to provide information about advance business trends worldwide, along with changing working parameters in an easy language.

Originally Published on https://www.breakfastleadership.com/

Michael Levitt Chief Burnout Officer

Michael D. Levitt is the founder & Chief Burnout Officer of The Breakfast Leadership Network, a San Diego and Toronto-based burnout consulting firm. He is a Keynote speaker on The Great Resignation, Quiet Quitting and Burnout. He is the host of the Breakfast Leadership show, a Certified NLP and CBT Therapist, a Fortune 500 consultant, and author of his latest book BURNOUT PROOF.