data engineers toolkit

The rapid adoption of cloud-based technologies, digital transformation and automation has created a demand for data engineers who help make sense of raw data and leverage it for improving various business outcomes.  Data engineers design and build the data infrastructure, develop algorithms, and build data pipelines.

Aiding them in these responsibilities are a range of programming languages, data management tools, data warehouses, and a variety of tools for data processing, data analytics, and AI/ML capabilities.

We list the Top 14 tools that must be a part of any data engineer’s toolkit.

  1. Kafka

This is an open-source event streaming platform that enables real-time streaming of data into a destination quickly. Kafka helps with data synchronization and messaging, among others. A popular tool for data collection and ingestion, it is used often to build ELT pipelines.

Where Netflix Uses Kafka

Netflix uses Kafka clusters as a core part of its Keystone Data Pipeline to process billions of messages per day.

According to the Kafka website, 80% of all Fortune 100 companies use Kafka. One of the biggest reasons for this is that it fits in well with mission-critical applications that require the use of APIs in cloud agnostic environments. Find out how in this free download.

  1. Python 

Python is a general-purpose programming language that has overtaken others to become the default choice of most data engineers. Its simple syntax makes it easy to learn and use, and is supported by several libraries. This makes it easy for it to be used in multiple use cases such as building data pipelines, creating ETL frameworks, for API interactions, automation, and data munging tasks.

Where Instagram Uses Python

Django, a high-level Python framework, fuels the Instagram success story as it is fast, secure, and scalable. According to Instagram’s Engineering blog, their data engineers use Python to deliver the “business logic” the serves 1 billion+ users.

  1. SQLQuerying

This is a very important aspect of data engineering and SQL (Structured Query Language) is most commonly used for accessing, updating, manipulating, and modifying data using queries and data transformation techniques. It functions well in building business logic models, executing complex queries, extracting metrics related to KPIs, and developing reusable data structures.

Where Rolls-Royce uses SQL

Rolls-Royce, currently has 13,000+ engines being used in commercial aircrafts around the world.

For the past 20 years, it has been using a data-driven process to maintain these engines. The company uses SQL and the Azure platform for its storage and querying needs to provide top class service and manage the data coming from many different types of aircraft equipment, including the engines.

  1. PostgreSQL

A popular open-source relational database, Postgres also has a vibrant community of developers and contributors. Built using an object-relational model, it is also very flexible, lightweight, and efficient.

Some of the attributes that make it popular include its data integrity, a variety of built-in and user-defined functions, and its extensive data capacity. It is ideal for data engineering workflows because PostgreSQL is designed to handle large datasets and offers high fault tolerance.

Where Reddit uses PostgreSQL

Reddit, a social news website with around 174 million registered users who can exchange views and information, uses PostgreSQL in two different ways. The ThingDB model is used to store data for most objects such as links, comments, accounts, and subreddits. It also uses traditional relational databases based on PostgreSQL to maintain and analyze traffic statistics, transactions, ad sales, and subscriptions.

  1. MongoDB

Data engineers love this NoSQL database because of its ease of use, flexibility, and ability to store and query structured and unstructured data, at scale. It is also very good for processing large volumes of data because of its distributed key-value store, MapReduce calculation capabilities, and document-oriented NoSQL capabilities. It also preserves data functionality while enabling horizontal scale.

Where Toyota Material Handling uses MongoDB

As part of its digital transformation, Toyota Material Handling Europe connected its 100,000 trucks with integrated telematics and shifted from a monolithic codebase to microservices

All these changes are driven by MongoDB Atlas, a fully-managed, global cloud database service from MongoDB, enabling latency, automatic scalability, security, compliance, and improved developer productivity, among others.

  1. Apache Spark

This is a commonly used tool for stream processing, essential to querying data in real-time, be it from an IoT device or a website, or any other source. Essentially, it is a unified analytics engine for big data processing. It comes with built-in modules for streaming, SQL, machine learning, and graph processing

Where TripAdvisor uses Apache Spark

TripAdvisor, one of the world’s largest travel websites, uses Apache Spark to personalize customer recommendations to help tens of millions of customers plan a perfect trip. It helps with reading and processing reviews of hotels and other listings on TripAdvisor. 

Apache Spark supports many programming languages such as Java, Python, R, and facilitates large-scale data processing up to terabytes of streams in micro-batches. It enables optimized query execution and uses in-memory caching.

  1. Apache Airflow

Multi-cloud environments have added a layer of complexity in being able to leverage the full potential of data effectively. It requires job orchestration and scheduling to break data silos, create more streamlined workflows, and automate repetitive tasks for greater efficiency between teams. Data engineers find Apache Airflow effective to orchestrate and schedule their data pipelines.

Where Airbnb uses Airflow

Airflow is being used internally at Airbnb to build, monitor, and adjust data pipelines. In fact, Airflow was first built at Airbnb and then outsourced under the Apache license.

  1. Amazon Redshift

Amazon Redshift is a fully managed cloud-based data warehouse that can store large volumes of data. Data engineers can quickly integrate new data sources for faster analytics.

Using standard SQL, one can combine large volumes of structured and semi-structured data from different data warehouses, operational databases, and data lakes with Redshift.

Where Nasdaq uses Redshift

As automated trading platforms have entered the market, the pace and volume of transactions have grown. 

In 2014, Nasdaq migrated from a legacy, on-premises data warehouse to an Amazon Web Services (AWS) data warehouse powered by an Amazon Redshift cluster to increase scale and performance and lower operational costs. 

In the next four years, the Amazon Redshift cluster grew to 70 nodes as the solution was expanded to support all the markets in North America. By 2018, the solution was ingesting financial market data ranging between 30 billion and 55 billion records ,of nearly 4 terabytes, from thousands of sources every night.

  1. Amazon Athena:Analysis of unstructured, semi-structured, and structured data stored in Amazon S3

Amazon Simple Storage Service, used for ad-hoc querying with standard SQL, is facilitated by this interactive query tool.

It’s a serverless tool which doesn’t need any infrastructure to be set up or managed. It also does not require complex ETL jobs to prepare data for analysis. Therefore, data engineers love it as it enables analyzing large datasets quickly.

Where Movable uses Athena

Movable Ink offers a real-time personalization solution for marketing emails based on a wide range of user, device, and contextual data to improve response rates and customer experiences. Amazon Athena serverless query service helps the business to analyze data stored in Amazon S3 to draw insights that help improve customers’ marketing campaigns.

  1. Snowflake

One of the reasons why data engineers choose this cloud-based data warehousing platform is the separation of storage and compute options. While helping with ingesting, transforming, data cloning, and data analytics, it also supports third-party tools.

Where Western Union uses Snowflake

Western Union, for instance, uses Snowflake because it provides a single source of truth with multi-cloud support, lowers the time for system maintenance and upkeep while increasing the time available for analytics. It also provides connectivity to many BI tools such as ThoughtSpot, Tableau, and others, for developing meaningful data visualizations and reports.

  1. Looker

Looker along with Google Cloud’s data analytics platform helps reveal the true power of data through powerful, fresh insights. Its real-time dashboards provide in-depth, consistent analysis for effective and informed decision-making. It provides unified access for successful outcomes and helps create custom apps to deliver unique data experiences based on the needs of your business.

Where Car Next Door uses Looker

Car Next Door, an Australian peer-to-peer car rental service company, was able to leverage Looker dashboards to draw insights from marketing data and experience a 3x increase in organic search traffic while reducing the CPA (cost per action) from SEM from $100 to $30.

  1. BigQuery

BigQuery is a serverless, scalable, and cost-effective multi-cloud data warehouse that provides insights with real-time and predictive analytics and ensures business agility.

With real-time Query data streaming, businesses can gain visibility into all their processes, forecast business outcomes leveraging built-in machine learning, and share the insights securely across the organization.

Where Twitter uses Google BigQuery

Twitter uses BigQuery to support ad-hoc and batch queries, data ingestion and transformation, data governance, metadata management, and privacy compliance.

  1. Tableau

Tableau is an interactive data visualization tool, helping transform raw data into an easily understandable format. Even non-technical users can create customized dashboards without the help of IT, with visualizations being created as dashboards and worksheets. It enables data blending, real-time analysis, data visualization, and data collaboration.

Where Charles Schwab, uses Tableau

At the financial services company Charles Schwab, 16,000 people use the BI tool every day for a variety of activities such as monitoring client activity and identifying outreach opportunities to improve client experience.

  1. PowerBI

Power BI, a collection of software services, apps, and connectors, integrates data from unrelated sources to provide coherent, visually immersive, and interactive insights. It enables creating, sharing, and consuming insights for improving business performance. 

Where Nestlé uses PowerBI

Nestlé, a transnational food and beverage company, implemented Microsoft Power BI to empower its employees to make data-driven decisions. It is now used by more than 45,000 employees across the company, all over the world.

In our ‘Data Engineer’s Toolkit’ blog collection, we will be looking at each of these tools closely, elaborating on their features that make them so well-suited for the purpose.

About Merit Group

At Merit Group, we work with some of the world’s leading B2B intelligence companies like Wilmington, Dow Jones, Glenigan, and Haymarket. Our data and engineering teams work closely with our clients to build data products and business intelligence tools. Our work directly impacts business growth by helping our clients to identify high-growth opportunities.

Our specific services include high-volume data collection, data transformation using AI and ML, web watching, and customized application development.

Our team also brings to the table deep expertise in building real-time data streaming and data processing applications. Our expertise in data engineering is especially useful in this context.

If you’d like to learn more about our service offerings or speak to a Kafka expert, please contact us here:

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