Alexander Alten

The right product development and management methodology always depends on your goals and geography. But if you want to make insane growth happen, obsess over your customers, find the right balance between strategy and flexibility, and implement a culture of constant learning.

Strategic AI implementation enables hyper-personalization and proactive service to exceed customer expectations. It also attracts top talent interested in working on cutting-edge projects and augments human capabilities.

Artificial intelligence (AI) and machine learning (ML) have become inescapable tech buzzwords. Yet, for many businesses, the actual value of these technologies can still feel out of reach. Is the cost of inventory mishaps eating away at your profit margins? Or are you… Show more

Strategic AI implementation enables hyper-personalization and proactive service to exceed customer expectations. It also attracts top talent interested in working on cutting-edge projects and augments human capabilities.

Artificial intelligence (AI) and machine learning (ML) have become inescapable tech buzzwords. Yet, for many businesses, the actual value of these technologies can still feel out of reach. Is the cost of inventory mishaps eating away at your profit margins? Or are you struggling to keep up with changing customer expectations? It's time to move past the hype and focus on how AI/ML can deliver tangible improvements and drive ROI in the real world. Show less

Transforming raw data into actionable insights requires the right approach. ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are the leading methods for streamlining data preparation. ETL ensures upfront data quality, ideal for regulated industries or structured data. ELT prioritizes speed and flexibility for rapid insights. This blog post offers a clear breakdown of ETL and ELT, guiding developers on choosing the right approach based on project needs, data types, and… Show more

Transforming raw data into actionable insights requires the right approach. ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) are the leading methods for streamlining data preparation. ETL ensures upfront data quality, ideal for regulated industries or structured data. ELT prioritizes speed and flexibility for rapid insights. This blog post offers a clear breakdown of ETL and ELT, guiding developers on choosing the right approach based on project needs, data types, and compliance. We also explore the future of data integration, highlighting the potential of hybrid models, real-time transformation, and federated learning for secure AI development. Show less

Understand data maturity's importance for modern businesses. Discover why your business might lag and how to advance with data handling and analytics. This post explores the concept of data maturity and its crucial role in today's data-driven business landscape. We'll delve into the key areas businesses need to focus on, the reasons some fall behind, and how to bridge the gap.

Modern product management isn’t just about adding features or functionalities. It’s a holistic approach that puts the digital customer at the center of everything you do. This means understanding your customers and their ever-evolving needs. To do this, you’ll need to dive deep into your customers’ behavior, preferences and pain points through multiple channels, such as customer research, data analysis and feedback mechanisms.
By building a culture of empathy with your customers, your… Show more

Modern product management isn’t just about adding features or functionalities. It’s a holistic approach that puts the digital customer at the center of everything you do. This means understanding your customers and their ever-evolving needs. To do this, you’ll need to dive deep into your customers’ behavior, preferences and pain points through multiple channels, such as customer research, data analysis and feedback mechanisms.
By building a culture of empathy with your customers, your product managers will be able to translate your insights into actionable solutions that will drive product development and improve your overall customer experience (CX). Show less

Organizations investing in analytics, artificial intelligence (AI), and other data-driven efforts face a rising challenge: a lack of integration across data sources, which limits their ability to extract actual value from these investments. To enable greater business insights, IT and business leaders must eliminate these data silos, some of which are operational and others of which are cultural. A large percentage of organizations and their leadership teams understand the value of data and are… Show more

Organizations investing in analytics, artificial intelligence (AI), and other data-driven efforts face a rising challenge: a lack of integration across data sources, which limits their ability to extract actual value from these investments. To enable greater business insights, IT and business leaders must eliminate these data silos, some of which are operational and others of which are cultural. A large percentage of organizations and their leadership teams understand the value of data and are working to develop a modern data strategy. Show less

Databloom.ai and the Blossom Development Environment are revolutionizing the field of data science by providing powerful tools and workflows to streamline data analysis and modeling. These tools enable data scientists to work more efficiently and effectively, leading to faster insights and better decision-making. Learn more about how Databloom.ai and the Blossom Development Environment are changing the game in our latest article.

Generative AI is a rapidly advancing field that holds the promise of revolutionizing the way we interact with technology. From generating high-quality digital images to creating realistic videos, or NLP-based text and information processing algorithms, the potential applications are endless. However, as we all know, with any new technology comes ethical concerns and the obligation to ensure that it is used for the greater good. One, if not the most threatening, of the significant challenges… Show more

Generative AI is a rapidly advancing field that holds the promise of revolutionizing the way we interact with technology. From generating high-quality digital images to creating realistic videos, or NLP-based text and information processing algorithms, the potential applications are endless. However, as we all know, with any new technology comes ethical concerns and the obligation to ensure that it is used for the greater good. One, if not the most threatening, of the significant challenges posed by generative AI is the risk of bias in the algorithms and models that it creates.‍ Show less

Create more accurate digital twin models, which can lead to better predictions and more efficient maintenance of assets. Digital twin models can also be trained using federated learning, by combining data from different sensors and systems.

Federated Learning (FL) is a machine learning technique where a centralized model is trained on decentralized data, that is, data that is distributed across multiple devices, such as smartphones, laptops, and IoT devices.

Public cloud displacement is a subject we don’t discuss as often as we ought to. Many see relocating data and apps back from a public cloud provider to an enterprise data center as an admission that the initial decision to move the workloads to the cloud was a grave error. In my opinion, this is less of a failure than a hosting platform change depending on the current state of the economy. People frequently return to more conventional platforms because of the high expense of cloud computing… Show more

Public cloud displacement is a subject we don’t discuss as often as we ought to. Many see relocating data and apps back from a public cloud provider to an enterprise data center as an admission that the initial decision to move the workloads to the cloud was a grave error. In my opinion, this is less of a failure than a hosting platform change depending on the current state of the economy. People frequently return to more conventional platforms because of the high expense of cloud computing. You surly remember the article from Dropbox, explaining why they left their public infrastructure and went to a private cloud approach. Show less

Federated Learning (FL) is a machine learning method in which a model is trained on multiple devices, such as smartphones or edge devices, rather than a centralized server. The devices, also known as clients, train a model on their own data and then send updates to a central server. After that, the server aggregates the updates and returns the improved model to the clients. This procedure is repeated until the model achieves the desired level of precision.

As it focuses on delivering useful and safe data products, Data Mesh is a strategic approach to modern data management and a strategy to support an organization's journey toward digital transformation. Data Mesh's major goal is to advance beyond the established centralized data management techniques of using data warehouses and data lakes. By giving data producers and data consumers the ability to access and handle data without having to go through the hassle of involving the data lake or data… Show more

As it focuses on delivering useful and safe data products, Data Mesh is a strategic approach to modern data management and a strategy to support an organization's journey toward digital transformation. Data Mesh's major goal is to advance beyond the established centralized data management techniques of using data warehouses and data lakes. By giving data producers and data consumers the ability to access and handle data without having to go through the hassle of involving the data lake or data warehouse team, Data Mesh highlights the concept of organizational agility. Data Mesh's decentralized approach distributes data ownership to industry-specific organizations that use, control, and manage data as a product. Show less

How to build a chatbot system, which serves multiple functions and customers across the world, like in a bank? A chatbot stack typically uses NLP combined with multiple data source to provide a natural communication between humans and machines. The demand of Machine-Human interaction and human based communication has considerably increased and the forecasts of Gartner are a testament to it.

Scalytics combines our two open source projects wayang and infinimesh into one AI-IoT solution, fully open source and ultra-scalable. Our AIoT platform is built to make data privacy and data ownership possible, without any doubt. To achieve this, we have built our platform in a cloud-native way and entirely API driven. It allows our customers to integrate our cloud into their systems without compromising IT Security or even move to a public cloud provider. We understand ourselves as a stretched… Show more

Scalytics combines our two open source projects wayang and infinimesh into one AI-IoT solution, fully open source and ultra-scalable. Our AIoT platform is built to make data privacy and data ownership possible, without any doubt. To achieve this, we have built our platform in a cloud-native way and entirely API driven. It allows our customers to integrate our cloud into their systems without compromising IT Security or even move to a public cloud provider. We understand ourselves as a stretched workbench for any IoT related ideas our customers might have. Show less

IoT devices are getting more and more intelligent and can now create meshed networks by itself, switching from a sensor into an actor and transferring informations only for the meshed neighbors. For example a connected car could tell the future home that the homeowner will be at home in 5 minutes and the garage door and the door need to be unlocked in time, the lights need to be switched on and the grid operator needs to be informed that the wallbox now charges with 22KW. In near future this… Show more

IoT devices are getting more and more intelligent and can now create meshed networks by itself, switching from a sensor into an actor and transferring informations only for the meshed neighbors. For example a connected car could tell the future home that the homeowner will be at home in 5 minutes and the garage door and the door need to be unlocked in time, the lights need to be switched on and the grid operator needs to be informed that the wallbox now charges with 22KW. In near future this will happen over direct meshed information cells, operated by always connected devices, wearables, sensors, actors, mobile devices - short: everything. And all cloud provider offer dozens of solution to master the challenges, on the one, other or complete different way. Show less

These days it means more to understand business processes and to transform them into data-driven opportunities – using cloud technology when needed

For a lot, but certainly not all enterprises, a typical big data software is Apache Hadoop. Hadoop is the new legacy standard in enterprises when it comes to on-premise tools. Cloud-based big data is still fairly new for enterprises and cloud providers, as the technology has only risen up to become cloud-ready in the last two years or so… Show more

These days it means more to understand business processes and to transform them into data-driven opportunities – using cloud technology when needed

For a lot, but certainly not all enterprises, a typical big data software is Apache Hadoop. Hadoop is the new legacy standard in enterprises when it comes to on-premise tools. Cloud-based big data is still fairly new for enterprises and cloud providers, as the technology has only risen up to become cloud-ready in the last two years or so. Popular tools here are Apache Kafka, Apache Spark or Apache Flink – these are all run best on bare metal servers. Show less

Right now, the terms BigData and Hadoop are used as one and the same - often like the buzzword of buzzwords. And they sound mostly as a last time call, often made by agencies to convince people to start the Hadoop journey before the train leaves the station. Don’t fall into that trap.

In building the next generation of applications, companies and stakeholders need to adopt new paradigms. The need for this shift is predicated on the fundamental belief that building a new application at scale requires tailored solutions to that application’s unique challenges, business model and ROI.

Hyper-Converged Infrastructures are used by more than 50% of the interviewed businesses, tendentious increasing. But what does this mean for BigData solutions, and Hadoop especially? What tools and technologies can be used, what are the limitations and the gains from such a solution?

To build a production ready and reliable private cloud to support Hadoop clusters as well as on-demand and static I have made great experience with OpenStack, Saltstack and the Sahara plugin for Openstack.

This demo shows that's pretty easy today by using available tools to setup more or less complex data flows within a few hours. Apache NiFi is pretty stable, has a lot of sinks available and runs now 2 weeks in Google Compute, captured over 200 mio tweets and stored them in SolR as well as in HDFS.

To shed light on big data and its implications in the healthcare industry, we have compiled a list of big data related terms, along with their definitions. We hope it proves helpful in better understanding the scope of healthcare big data, as well as how to turn big data into practical data and insights that can lead to more successful campaigns.

Apache Hadoop comes more and more into the focus of business critical architectures and applications. Naturally SQL based solutions are the first to get considered, but the market is evolving and new tools are coming up, but leaving unnoticed.

To tune a Hadoop cluster from a DevOps perspective needs an understanding of the kernel principles and linux. The following article will describe the most important parameters together with tricks for an optimal tuning.

In November 2013 Facebook published their Presto engine as Open Source, available at GitHub. Presto is a distributed interactive SQL query engine, able to run over dozens of modern BigData stores, based on Apache Hive or Cassandra. Presto comes with a limited JDBC Connector, supports Hive 0.13 with Parquet and Views.

HDFS is designed to protect data in different ways to minimize the risk of data loss with a valuable write speed. This enables in some circumstances HDFS as a NAS replacement for large files with the possibility to quickly access the stored data.

Impala provides fast, interactive SQL queries directly on your Apache Hadoop data stored in HDFS or HBase. In addition to using the same unified storage platform, Impala also uses the same metadata, SQL syntax (Hive SQL), ODBC driver and user interface (Hue Beeswax) as Apache Hive.

Overview about the Apache Hadoop Ecosystem, written in german

Flume, the decentralized log collector, makes some great progress. Since the project has reached an Apache incubating tier the development on the next generation (NG) has reached a significant level.

Snappy is a compression and decompression library, initially developed from Google and now integrated into Hadoop. Snappy acts about 10% faster than LZO, the biggest differences are the packaging and that snappy only provides a codec and does not have a container spec, whereas LZO has a file-format container and a compression codec.

For some reasons it could be a good idea to make a hdfs filesystem available across networks as a exported share. Here I describe a working scenario with linux and hadoop with tools both have on board.