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I am a Data Engineer I love Python! I am based in Singapore I have good knowledge on cloud I have some knowledge on building Machine Learning Models I have helped 30+ companies use data effectively I have worked with Petabytes scale of data I write @ TowardsDataScience Nowadays I am focusing on learning Natural Language Processing Ask me about anything, I'll be happy to help.
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In today's fast-paced digital world, keeping up with the latest advancements in data engineering is crucial to stay ahead of the competition. With the amount of data collected every day increasing, data engineering plays an important role in guaranteeing data accuracy, consistency, and reliability for enterprises.
In this blog, we will be discussing the top 5 new data engineering technologies that you should learn in 2023 to stay ahead of the curve. Each of the technologies we will be looking at brings a unique set of capabilities and benefits to the table that can help businesses improve their data engineering processes and make better data-driven decisions. So, let’s dive in and learn!
Apache Superset is a modern, open-source data visualisation and exploration platform that allows businesses to analyse and visualise data from multiple sources in real-time. Apache Superset was initially launched in 2016 by Airbnb as an internal tool but was later open-sourced in 2017 and has since become a popular choice for businesses and organisations. Apache Superset is designed to be extremely scalable and capable of managing massive amounts of data without sacrificing performance.
The most notable feature about Apache Superset is its ability to connect to a wide range of data sources, including SQL-based databases, Druid, Hadoop, and cloud-based data warehouses such as Amazon Redshift and Google BigQuery. As a result, it is a very adaptable tool that can simply be integrated into existing data infrastructures.
Let’s explore some of the features of Apache Superset:
Data Visualisation: Provides various visualisation options, such as line charts, scatter plots, pivot tables, heat maps, and more. Users can customise these visualisations to suit their branding and style.
Advanced Analytics: In addition to data visualisation, Apache Superset also offers advanced analytics features, including predictive analytics and machine learning capabilities. This enables firms to acquire insights into their data and make well-informed decisions based on real-time data analysis.
Dashboard Sharing: Makes it easy for users to share their dashboards with others. Users can share dashboards via a URL or embed them in other applications using an iframe.
Query Building: Query builder interface enables users to create complex queries using a drag-and-drop interface. Users can also write SQL queries directly if they prefer.
Overall, Superset is anticipated to gain more popularity in 2023 as companies seek open-source substitutes for proprietary data visualisation software. If you're keen on data visualisation and reporting, Superset is an excellent tool to acquire knowledge.
Apache Iceberg is an open-source data storage and query processing platform that was developed to provide a modern, scalable, and efficient way of managing large datasets. It is made to accommodate a variety of workloads, such as batch and interactive processing, machine learning, and ad-hoc queries. Apache Iceberg was created by the team at Netflix and was open-sourced in 2018.
One of the most significant features of Apache Iceberg that makes it special is its ability to support schema evolution. As datasets grow and change over time, It's crucial to be able to add or remove columns from a database without interfering with already-running applications or queries. Apache Iceberg allows users to add or remove columns to a table without having to rewrite the entire dataset. This makes it easy to evolve and maintain data models as business needs change.
Let’s look over the benefits provided by Apache Iceberg:
Efficient Query Processing: Uses a columnar format that reduces the amount of data that needs to be read from disk, which improves query performance. It also supports predicate pushdown and other optimizations that further improve query performance.
Data Consistency: Combination of versioning and snapshot isolation ensures that readers and writers never interfere with each other. Data is always in a consistent state, even during updates or when multiple users are accessing the same data simultaneously.
Easy Integration: Designed to be easy to integrate with existing data processing frameworks, such as Apache Spark, Apache Hive, and Presto. It provides connectors for these frameworks, which makes it easy to start using Iceberg with minimal changes to existing code.
Scalability: It supports partitioning and clustering, which allows users to organise their data into smaller, more manageable chunks. This makes it easier to distribute and process large datasets across multiple nodes in a cluster.
Data Management: Provides a modern, efficient, and scalable way of managing large datasets. It makes it easy to store, organise, and query data, which can improve data quality and increase business agility.
Hence, Apache Iceberg should be learnt for its ability to handle large datasets efficiently and its support for schema evolution, which are critical for modern data management scenarios. It is also a popular technology used by many organisations, making it a valuable skill to have.
Great Expectations is an open-source Python library that provides a set of tools for testing and validating data pipelines. First launched in October 2019 as an open-source project on GitHub, it enables users to specify "expectations" for their data - assertions or limitations on how their pipelines should behave. These expectations can be simple rules, like checking for missing values or checking that a column contains only certain values, or more complex constraints, like ensuring that the correlation between two columns falls within a certain range. Additionally, the library offers a number of tools for visualising and documenting data pipelines, making it simple to comprehend and troubleshoot complex data workflows.
Several key features make Great Expectations a valuable tool for data engineers:
Expectation Library: Provides a comprehensive library of pre-defined expectations for common data quality checks. Users can also define their own custom expectations to meet specific requirements.
Data Documentation: Makes it easier to document and understand the data used in pipelines, providing data dictionaries that capture metadata, such as column descriptions, data sources, and data owners. This allows teams to collaborate and understand the data being used in their pipelines.
Data Validation: Offers a range of validation tools, such as data profiling, schema validation, and batch validation, which help users catch issues and errors in their pipelines before they cause downstream problems.
Extensibility: Easy integration with a wide range of data processing and analysis tools, such as Apache Spark, Pandas, and SQL databases. This allows users to use Great Expectations with their existing data stack and workflows.
Automation: Provides a suite of tools for automating the testing and validation of data pipelines, including integration with workflow management tools, such as Apache Airflow and Prefect. This enables users to automate the monitoring and validation of their pipelines to ensure data quality and reliability over time.
Data engineers should learn Great Expectations in 2023 because it offers a comprehensive suite of data validation, documentation, and automation tools. As data quality becomes increasingly important, Great Expectations provides a reliable solution for ensuring data integrity. Furthermore, its integration with popular data processing tools makes it a valuable addition to any data engineer's toolkit.
Delta Lake is an open-source storage layer that is designed to improve the reliability, scalability, and performance of data lakes. It was initially released in 2019 by Databricks and has since gained popularity among data teams and has become an important tool for managing and maintaining data lakes. Data dependability is provided by Delta Lake, which is built on top of Apache Spark, using a transactional layer to make sure that all data updates are atomic and consistent.
Delta Lake has several features to offer that make it a valuable tool for data teams:
ACID Transactions: Delta Lake uses atomic, consistent, isolated, and durable (ACID) transactions to ensure data reliability. This means that data changes are atomic and consistent, and can be rolled back in the event of a failure.
Schema Enforcement: Supports schema enforcement, which ensures that all data stored in the data lake conforms to a predefined schema. This helps to improve data quality and reduces the risk of errors and inconsistencies in the data.
Data Versioning: Supports data versioning, allowing users to track changes to their data over time. This helps to ensure data lineage and enables teams to audit and understand changes to their data over time.
Performance: Delta Lake is designed for performance and can support petabyte-scale data lakes. It also includes optimizations such as indexing and caching to improve query performance.
Open Source: Delta Lake is an open-source project, meaning that it can be used and contributed to by the wider community. This helps to drive innovation and ensures that Delta Lake remains a flexible and evolving solution.
Since its debut, Delta Lake has grown significantly in popularity, and by 2023, data engineers are expected to get familiar with this tool. With more businesses switching to cloud-based solutions for their data infrastructure, Delta Lake is becoming an increasingly important tool for data teams owing to its support for cloud storage services and its capacity to handle difficult data management problems. Furthermore, as more businesses seek to leverage the power of big data and advanced analytics to drive informed decision-making, the need for reliable and scalable data management solutions like Delta Lake will only continue to grow.
ChatGPT is a large language model developed by OpenAI and released in June 2020, It is based on the GPT-3.5 architecture and designed to generate human-like responses to natural language queries and conversations. The model is capable of understanding and generating responses in multiple languages, and it can be fine-tuned on specific domains or tasks to improve its performance. ChatGPT's ability to perform multiple tasks such as text classification, sentiment analysis, and language translation can help data engineers to gain insights from unstructured data.
One of ChatGPT's key strengths is its capacity to generate open-ended responses to inquiries and conversations, enabling users to have impromptu talks with the model.ChatGPT is trained on a massive corpus of text data, which allows it to generate responses that are contextually relevant and grammatically correct.
Some valuable features of ChatGPT that make it an all-rounder are:
Contextual understanding: ChatGPT can understand the context of a conversation and generate responses that are relevant to the topic being discussed.
Machine learning: Based on deep learning algorithms that enable it to learn and improve over time based on the data it processes.
Customization: ChatGPT can be fine-tuned on specific domains or tasks to improve its accuracy and effectiveness.
Content Creation: Used to generate content for websites, blogs, and social media posts. This can save content creators time and effort while ensuring that the content generated is high-quality and engaging.
Language translation: The ability to understand and generate responses in multiple languages makes it a valuable tool for language translation services.
ChatGPT is an AI-powered chatbot that can help data engineers and other professionals automate repetitive tasks, streamline workflows, and improve productivity. As AI and natural language processing continue to advance, ChatGPT is poised to become an increasingly valuable tool for data engineering teams in 2023 and beyond. Learning how to use ChatGPT can help data engineers stay ahead of the curve and enhance their data engineering capabilities.
In conclusion, data engineering is an ever-evolving field, and staying up-to-date with the latest technologies and tools is crucial to gain a competitive edge in the industry. From Apache Superset which can provide powerful data visualisation capabilities to Apache Iceberg which offers easy and efficient table evolution; these technologies can help data engineers work more efficiently and effectively. Great Expectations can ensure data quality and maintain data integrity, while Delta Lake provides a reliable and efficient way to manage big data. On the other hand, ChatGPT offers an innovative and interactive way to create conversational AI models. By learning these technologies, data engineers can stay ahead of the curve and be better equipped to handle the complex challenges of data management and analysis. So, don't wait - start exploring these exciting tools and stay on top of the latest trends in data engineering in 2023 and beyond.
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