The best data engineering tools and technologies in 2021

Many organizations are eager to adopt data science into their decision making processes. However, they often forget the foundational work necessary to make that happen–data collection, data literacy, and data infrastructure. All these are crucial in building intelligent data solutions. When it comes to the hierarchy of data science needs, data engineering ranks as one of the most important disciplines. It’s responsible for finding trends in data and developing algorithms to help data scientists and data analysts make sense out of raw data.

To build splendid infrastructures, data engineers require a blend of different programming languages, data warehouses implementation, and a wide range of data engineering tools required for data processing and analytics.

In this article, we discuss the most important tools for data engineers that every engineer should master. You’ll also learn about the key features to keep in mind when evaluating data engineering tools and how to compare them against each other.

What are the key features of data engineering tools and technologies?

Here are some of the most crucial features that data engineering tools and technologies must possess:

• Data lineage/traceability: It should be able to trace where your data is coming from, where it’s going, and what transformation it has undergone as it flows through multiple processes.
• Extract-transform-load (ETL): Should move your data from the source to the data warehouse•Data transformations: Converts data from one structure/format to another.
• Metadata support: Should facilitate and support resource identification, discovery and organization.
• Workflow automation: Should support workflow templates that can be reused for efficiency and to save time.
• No-code features: Should feature user-friendly drop and drag wizard that non-coders can use.
• Batch or stream processing: Should process a large volume of data in a batch within a given time span, or process continuous streams of data immediately as it’s being produced.
• Reporting and data visualization capabilities: Enable you to convert data into reader-friendly graphics and charts.

It might be interesting for you: What is the future of data engineering?

How to compare data engineering tools against each other?

Here’s a summary of comparison criteria that you can use when evaluating different data engineering tools:

• Value for money: Is the tool’s price worth its features and capabilities? And is the pricing flexible, clear, and transparent?
• User Interface (UI): Does the tool’s point of interaction make your experience easy and intuitive? And does it require minimum effort to receive the desired outcome?
• Usability: How easy is it to learn and master the tool? Does the provider offer training and user support services?
• Integrations and extensibility: Which built-in integrations does the tool offer? Is it easy to link it with other tools? And is it compatible with your data sources?
• Setup time: How long does it take to set up the tool and for you to attain maximum usability for your use case? Is it on-premises or cloud-based?

You may find this article interesting: Data Engineering in Startups: How to Manage Data Effectively

The most popular data engineering tools and technologies in 2021

Now that you know the most important features that data engineering tools should possess and how to compare the tools and technologies against each other, we can now discuss some of the best tools and technologies in 2021.

Apache Spark

Apache spark[1] is a fast, flexible, developer-friendly analytics engine used to process large volumes of data. It uses optimized query execution and in-memory caching to analyze queries fast against data of any given size. Spark provides high-level Application Programming Interfaces (APIs) with Python, Scala, Java, and R, taking some of the programming burdens off your shoulders.

It also supports other high-level tools such as spark SQL, for structured data processing, GraphX[2] for graph processing, and MLlib[3] for machine learning.

Source: proedu.co

Apache Spark Architecture

It has a well-defined layout where all its components and layers are coupled loosely. The two main components in Spark’s architecture are:

• Driver: The driver converts codes into tasks that can be distributed across multiple worker nodes.
• Executors: It runs on the worker nodes to execute the assigned tasks.

Some of the features that make Apache Spark a powerful data engineering tool include:

• Speed: For large scale data processing, Spark’s speed can be 100 times faster than Hadoop MapReduce. It’s able to achieve this speed, thanks to its in-memory cluster computing feature.
• Powerful caching: It has a simple programming layer that gives it disk persistence and powerful caching capabilities.
• Deployment: It can be deployed through Hadoop, Mesos, or its own cluster manager.
• Real-time computation: Its in-memory computation feature makes it easy for Spark to offer real-time data computation and low latency.

SQL

Structured Query Language (SQL)[4], is a programming language used to create, manipulate, and query data in relational databases. It’s mainly used by database administrators, developers and data analysts to write data integration scripts and run analytical queries. On the simplest level, this tool has a few commands, i.e., Select (grabs the data), insert (adds data into a database), update (changes information, and delete (deletes information).

There are other commands responsible for creating, modifying, and modifying databases. Some of the common relational databases that use SQL include: Sybase, Oracle, Microsoft SQL Server, Ingress, and Access, among others.

SQL has a wide range of use cases, from e-commerce sites to government databases. Its popularity has continued to grow due to the following reasons:

• It allows users to retrieve data from relational database management systems
• It allows users to create and manipulate databases and their tables
• It has strong security features that allow you to set constraints or permissions on tables, views, columns, and stored procedures
• It allows users to define and modify data stored in relational databases

One of the recent offshoots of SQL is SQL-on Hadoop[5]. It’s an emerging technology used by organizations with big data architectures built on Hadoop systems. It combines traditional SQL-style querying with recent Hadoop data frameworks to make it easier for a wide range of data science professionals to work with Hadoop on commodity computing. SQL-on –Hadoop is becoming a regular constituent of Hadoop deployments as it makes it easier for users to implement the Hadoop framework on current enterprise systems.

Snowflake

Snowflake is a cloud-based data warehouse that’s provided as Software-as-a-Service (SaaS). This means that it’s not built on any existing data platform. Instead, it uses an SQL database engine, making it easier to understand and use. There’s no physical hardware or software that you need to install, configure, or manage with snowflake.

And therefore, it’s ideal for businesses that do not want to channel resources into the set-up and maintenance of in-house servers.

Source: snowflake.com

Snowflake integrates big data sets from multiple sources, processes it, and delivers analytical reports upon demand. It’s fast, user-friendly, and more flexible than traditional data warehouses. What sets it apart from other data warehouses is its unique architecture and data sharing capabilities.

The architecture separates compute and storage functionalities, enabling users to use and pay for its storage and computation separately. And its sharing capability makes it easy to share data with other snowflake accounts in real-time.

This tool also enables you to integrate it with other tools such as Elastic Compute Cloud (EC2), and Amazon Simple Storage Service (S3). It provides an enterprise solution that streamlines the gathering, processing and use of big data.

Mongodb(OR NOSQL)

Mongo DB[6] is an open-source, document-oriented Database Management System(DBMS) used to store large volumes of data. Being a document-oriented database, it deals with documents rather than defined tables of information. It’s able to parse data from documents and store it for retrieval.

MongoDB is used as an alternative to the classical relational databases since it supports various forms of data. It’s simple, dynamic, flexible, and can be used to store and query huge volumes of both structured and unstructured data.

Some of the features that make MongoDB one of the most preferred databases include:

• Its document structure is in line with how developers construct their objects and classes in different programming languages.
• It has a data model that allows you to represent hierarchical relationships and easily store complex structures.
• MongoDB is very scalable. It supports horizontal scaling through sharding, allowing you to add more instances to increase capacity when required. This makes it a great database for companies that run big data applications.
• The documents can be created on the fly. They don’t require predefined schemas. This allows users to create any number of fields in a document.

Panoply

Panoply[7] is a fully-managed data warehouse that provides end-to-end data management. It’s based on an Extract-Load-Transform (ETL) model, which loads raw data into the warehouse using in-built data source integrations. And being a cloud-based data platform, it’s easy to store, sync and access your data on panoply.

The data warehouse supports zero-code integrations with data sources for seamless syncing and allows users to schedule updates so that data can remain fresh and ready for immediate analysis. It’s easy to use, requires low maintenance, and offers granular control of how individual data sets are stored.

Some of the key features of this platform include:

• Code-free data integrations to make the syncing process easier
• Connections to major analytical and business intelligence tools
• Automated configurations

Allstacks

Allstacks[8] is the best data engineering tool when it comes to software intelligence. It’s a Value Stream Intelligence that seeks to build organizational trust by aligning engineering, product, and project management office (PMO) teams through predictive risk analysis and forecasting. It gives insights into all the tools and projects in an organization, so users can detect risks that may impact important operations and resolve them in real-time.

Allstacks aggregates data into visual dashboards, including milestone reports, portfolio reports, and pull request cycle time charts. This enables organizations to learn from previous work patterns and product delivery outcomes.

One main factor that has made Allstacks one of the most preferred data engineering tools is the fact that it integrates with a wide range of software development lifecycle tools. These include project management tools, communication tools, source code management tools, among others.

The bottom line–tools for data engineers

The data engineering landscape is constantly evolving, and emerging technologies supporting the field are growing at a rapid clip. The tools and technologies that we’ve discussed above are just the tip of the iceberg, as other great tools didn’t make it to the list. These tools are important not only for data engineers but also for other data science professionals since they make the job of managing, storing, and analyzing data quite easier.

If you’re looking for a data engineer or data engineering tools–feel free to reach out! The Addepto team is experienced in data engineering services, and we have many amazing data engineers on board. We will gladly support you with our experience and knowledge!

References

[1]Spark.apache.org. Unified Engine for Large-scale Data Analytics. URL: https://spark.apache.org/. Accessed Oct 27,2021.
[2] Manning.com. Spark GraphX in Action. URL: https://www.manning.com/books/spark-graphx-in-action. Accessed Oct 27, 2021.
[3] Spark.apache.org. Mlib Guide. URL: https://spark.apache.org/docs/latest/ml-guide.html. Accessed Oct 27, 2021.
[4]Techtarget.com. SQL. URL: https://searchdatamanagement.techtarget.com/definition/SQL. Accessed Oct 27, 2021.
[5] Techtarget.com. SQL-on-Hadoop. URL: https://searchdatamanagement.techtarget.com/definition/SQL-on-Hadoop. Accessed Oct 27, 2021.
[6] Kdnuggets.com. MongoDB Basics. URL: https://www.kdnuggets.com/2019/06/mongo-db-basics.html. Accessed Oct 27, 2021.
[7] Panoply.io. The Easiest Could Data Platform. URL: https://panoply.io/. Accessed Oct 27, 2021.
[8] Allstacks.com. Build Software Like You Mean Business. URL: https://www.allstacks.com/. Accessed Oct 27, 2021.