What emerging data engineering and visualization technologies can you use to improve business insights?

For Data Engineering Data mesh Architecture, Batch & Real-time data streaming platforms, DataOps & MLOps and Serverless computing are pretty useful for most use-cases. While Tableau, Data story telling Platforms like Quicksight and Graph visualization are preferred tools for business insights.

Firstly always strong in basics like python and sql . Then familiar with cloud . Then concentrate on latest cloud certifications . For visualization tableau power bi etc are best

There are some tools and techniques in data work that can help businesses understand things better. For example, Apache Kafka helps to handle data in real-time, Apache Airflow helps to organize tasks, and Apache Spark is good for working with large amounts of data. Then there are tools like Tableau, Power BI, and Looker for advanced data visualization and analytics. Also, using machine learning tools like TensorFlow or PyTorch can make predictions that help businesses even more.

In my opinion, this is constantly changing even more so now with the use of GenAI. Regarding the technologies that help improve business insights, they would be: For Data Visualization (Power BI and Tableau) and For Data Engineering (Azure Data Factory, Azure Synapse and Databricks). As I said, due to the advancement and constant changes of platforms, I believe there are trends for platforms to be end-to-end, that is, from data extraction to visualization for the end user to consume. We can already see this trend in the new Microsoft Fabric tool launched in 2023, which brings several possibilities by covering the entire Data process on a single SaaS platform.

Data Mesh is an approach to data architecture that emphasizes decentralization and domain-driven design. It helps organizations manage and scale their data infrastructure more effectively, enabling better access to and utilization of data for business insights.

Stream processing technologies, such as Apache Kafka or Apache Flink, allow you to process and analyze data in real-time. This can be especially useful for monitoring and reacting to changing business conditions as they happen.

Technologies like Kafka, Spark, and Flink enable real-time data processing directly as it flows in from sources. This approach allows data engineers to conduct complex analytics—like anomaly detection and sentiment analysis—immediately, bypassing the need for data storage. The advantage of stream processing is the significant reduction in latency, providing the ability to scale effectively and respond promptly to data-driven insights. Businesses can leverage these insights for timely decision-making and to improve operational efficiency. The real-time nature of stream processing is instrumental for organizations looking to enhance their data visualization capabilities and gain a competitive edge with up-to-the-minute business intelligence.

Stream processing helps different type of end clients in different ways.. Analyzing social media posts helps to understand user behaviour Analyzing sensor data helps to identify the status of machines/weather monitoring devices etc.

By leveraging stream processing, businesses can gain immediate insights from high-velocity data streams, enabling timely decision-making and proactive responses to changing market conditions. Additionally, stream processing can enhance data visualization capabilities by enabling the creation of real-time dashboards and interactive visualizations that provide up-to-the-minute insights into key business metrics and performance indicators.

Amazon Kinesis is excellent for stream processing on AWS. It offers Data Streams for custom applications handling large data volumes, Data Firehose for simplified data loading into stores and analytics, and Data Analytics for SQL-based real-time processing. With scalability, durability, and low latency, it suits log/event processing, analytics, and ML tasks. However, its suitability depends on specific requirements like data volume and integration needs.

There is a significant impact of using low-code or no-code tools as time-saving for a project. One should know the use case and place of implementation of these tools. No-code tools save much time when building an ETL, and low-code tools help write custom transformations to satisfy business requirements. It is also critical to understand the cost of these tools; we do not need to use AWS/Azure for all data engineering projects unless the data is substantial. Alteryx, Microsoft Fabric, and Power Apps are cost-effective tools for less data. These tools simplify error handling by providing clear error messages when a data flow or pipeline fails to refresh. Moreover, using these tools saves time and money on data engineering tasks.

Low-code and no-code tools enable business users to create data pipelines, dashboards, and visualizations without requiring deep technical expertise. This democratizes access to data and empowers more people in the organization to derive insights from data.

These platforms democratize data analysis and application development, allowing non-technical users to create and manage databases, applications, and workflows without deep coding knowledge. Tools like Microsoft Power BI, Tableau, and Google Data Studio can significantly accelerate the deployment of data solutions and analytics, reducing the time from insight to action.

Most users of low-code platforms, such as Excel and PowerApps, write programs in domain-specific formula languages to carry out nontrivial tasks. Often users can write most of the program they want, but introduce small mistakes that yield broken formulas. These mistakes, which can be both syntactic and semantic, are hard for low-code users to identify and fix, even though they can be resolved with just a few edits. We formalize the problem of producing such edits as the last-mile repair problem. To address this problem, there are tools under development (like LaMirage, a LAst-MIle RepAir-engine GEnerator), that combine symbolic and neural techniques to perform auto repair in low-code formula languages

I saw several business users working with low-code/no-code tools, as these tools doesn't need much technical expertise. These are easy to use and interact with minimal training and learning.

Augmented analytics uses machine learning and natural language processing to automate data preparation, insight discovery, and visualization. This can help business users uncover hidden patterns and insights in data more quickly and easily.

Augmented analytics use machine learning and AI to automate data preparation, insight generation, and insight explanation. This technology can dramatically speed up the process of data analysis and uncover insights that might be missed by human analysts. Tools incorporating augmented analytics can also suggest the most effective types of visualizations for the given data.

Tableau, Microsoft Power BI, and Qlik Sense offer augmented analytics with AI-driven insights. IBM Watson Analytics provides predictive modeling. Sisense simplifies analysis with AI recommendations. Looker, ThoughtSpot, and Yellowfin offer natural language querying and automated insights. Oracle Analytics Cloud and SAS Visual Analytics enhance data exploration with AI and machine learning. Choose based on specific needs and integration capabilities.

Augmented analytics offers a transformative approach to data analysis, providing organizations with enhanced insights, simplified data preparation, and democratized analytics capabilities. By leveraging ML and AI algorithms, it accelerates decision-making processes and improves the accuracy of insights by identifying anomalies and patterns that may go unnoticed through traditional methods. It scales seamlessly to handle large volumes of data and empowers non-technical users to perform advanced analytics tasks independently. With personalized insights tailored to individual user needs and continuous learning capabilities, it enables organizations to stay agile and make data-driven decisions with confidence.

In my past experience, Tableau has allowed analytic users with some understanding of data to explore data sets fairly rapidly. In testing it out, I was able to download a copy and ingest a data set and produce rapidly some insights that were presented to executives on a pressing issue within hours. It is intuitive. In my opinion, I haven't seen a lot of engagement with data engineers or data scientists in their workflow except to surface the data for less technical users like analysts through Tableau. I've seen use of Power BI which has more data querying capabilities in small data sets but it is less advanced in my opinion in visualization.

Technologies such as Virtual Reality (VR) and Augmented Reality (AR) are introducing new ways to interact with and understand data. Immersive visualization can provide a more intuitive understanding of complex data sets and is particularly useful in fields like product design, architecture, and urban planning.

Immersive visualization technologies, such as virtual reality (VR) and augmented reality (AR), can provide a more immersive and interactive way to explore data. This can lead to deeper insights and better understanding of complex datasets.

I would hedge my bets on 1) Augmented reality and 2) Digital twins. In my view, cost related pressure will drive industries towards leveraging digitalization as a means of getting more output from limited manpower. Companies will move towards a setup where key resources and people are centralised. For this AR will become must. The centralisation of resources will necessitate advance planning for which digital twins will become necessary.

VR and AR technologies provide greater end user experience. These will help in detailed and indepth understanding of e-commerce products.

- Ethical use of GenAI tools for developing debugging🤖 - By processing data closer to its source, businesses can significantly reduce latency enable faster decision-making. (Eg. IOT devices)⚙️ - Cross Platform Integration and collaborative platforms like Databricks (Brings in Data Engineer and Analytical Engineer)🤝 - Use of multi-dimensional tool and data management platforms where you could do from cleaning till insights. (Using Tableau for Forecasting as well as insights) 📊

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While embracing these emerging technologies, it’s crucial to maintain a strong foundation in data governance and quality. Ensure that your data is clean, well-organized, and secure. Additionally, consider the scalability of these technologies and their integration with existing systems. Training and change management will also be key, as adopting new technologies often requires shifting mindsets and developing new skills within the team. Finally, always align your technology choices with your specific business needs and goals to ensure that they truly add value to your decision-making processes.

Since almost all the orgs have created their on data lakes, there is so much happening around data modelling and generating quick insights on top of them, fast paced dashboards, genAI being used to bring results to businesses who wont need engineering background to generate reports, i think how you model your data, how quickly you clean it and how securely you make it accessible are the new backbones on which modern applications will be built, text to sql models are getting developed on fast pace which will change the paradigm in 2-3 years.

I would like to add cognitive insight, GenAI with self built LLM and Artificial Intelligence with ML capabilities are important emerging technologies to part of this article.