#data pipeline

[mlearningai]

Numbers in free verse?

This piece recasts data pipelines as unstoppable storytellers.

Your views on data will shift.

Read more here:

[prollcmatchdata]

Data pipeline| Match Data Pro LLC

Maximize efficiency with Match Data Pro LLC's automated data pipeline solutions. Seamlessly configure and execute end-to-end workflows for profiling, cleansing, entity resolution, and fuzzy matching. Trigger jobs manually, via API, or on a schedule with real-time notifications and email confirmations.

Data pipeline

[lisakeller22]

Explore the blog to learn how to develop a data pipeline for effective data management. Discover the key components, best practices, applications, hurdles, and solutions to streamline processes.

[jcmarchi]

Top 5 areas in the data pipeline with the least responsiveness

New Post has been published on https://thedigitalinsider.com/top-5-areas-in-the-data-pipeline-with-the-least-responsiveness/

Top 5 areas in the data pipeline with the least responsiveness

Data pipelines are critical for organizations handling vast amounts of data, yet many practitioners report challenges with responsiveness, especially in data analysis and storage.

Our latest generative AI report revealed that various elements within the pipeline significantly affect performance and usability. We wanted to investigate what could be affecting the responsiveness of the practitioners who reported issues. 

The main area of data workflow or pipeline where practitioners find the least responsiveness is data analysis (28.6%), followed by data storage (14.3%) and other reasons (14.3%), such as API calls, which generally take a significant amount of time.

What factors have an impact on that portion of the data pipeline?

We also asked practitioners about the factors impacting that portion of the pipeline. The majority (58.3%) cited the efficiency of the pipeline tool as the key factor. This could point to a pressing need for improvements in the performance and speed of these tools, which are essential for maintaining productivity and ensuring fast processing times in environments where quick decision-making is key.

With 25% of practitioners pointing to storage as a significant bottleneck after the efficiency of the pipeline tool, inadequate or inefficient storage solutions can impact the ability to process and manage large volumes of data effectively. 

16.7% of practitioners highlighted that code quality disrupts the smooth operation of AI pipelines. This can lead to errors, increased downtime, and complicated maintenance and updates. 

Code quality

The quality of the code in the data pipeline is key to its overall performance and reliability. High-quality code often leads to fewer errors and disruptions, translating to smoother data flows and more reliable outputs. 

Examples of how high code quality can enhance responsiveness:

1. Error handling and recovery

2. Optimized algorithms 

3. Scalability

4. Maintainability and extensibility

5. Parallel processing and multithreading

6. Effective resource management 

7. Testing and quality assurance

Efficiency of pipeline tool

Efficient tools can quickly handle large volumes of data, helping to support complex data operations without performance issues. This is an essential factor when dealing with big data or real-time processing needs, where delays can lead to outdated or irrelevant insights. 

Examples of how the efficiency of pipeline tools can enhance responsiveness:

Data processing speed 

Resource utilization

Minimized latency

Caching and state management

Load balancing

Automation and orchestration

Adaptability to data volume and variety

Storage

Storage solutions in a data pipeline impact the cost-effectiveness and performance of data handling. Effective storage solutions must offer enough space to store data while being accessible and secure. 

Examples of how storage can enhance responsiveness:

Data retrieval speed

Data redundancy and backup

Scalability

Data integrity and security

Cost efficiency

Automation and management tools

Integration capabilities

What use cases are driving your data pipeline?

What use cases are driving your data pipeline?

We also asked respondents to identify the specific scenarios or business needs that drive their data pipelines’ design, implementation, and operation to understand the primary purposes for which the data pipeline is being utilized within their organizations.

Natural language processing, or NLP, was highlighted as the main use case (42.8%), with an even distribution across the other use cases. This could be due to businesses increasing their operations in digital spaces, which generate vast amounts of textual data from sources like emails, social media, customer service chats, and more.

NLP

NLP applications require processing and analyzing text data to complete tasks like sentiment analysis, language translation, and chatbot interactions. Effective data pipelines for NLP need to manage diverse data sources like social media posts, customer feedback, and technical documents.

Examples of how NLP drives data pipelines:

Extracting key information from text data

Categorizing and tagging content automatically

Analyzing sentiment in customer feedback

Enhancing search and discovery through semantic analysis

Automating data entry from unstructured sources

Generating summaries from large text datasets

Enabling advanced question-answering systems

Image recognition

Image recognition analyzes visual data to identify objects, faces, scenes, and activities. Data pipelines for image recognition have to handle large volumes of image data efficiently, which requires significant storage and powerful processing capabilities. 

Examples of how image recognition drives data pipelines:

Automating quality control in manufacturing

Categorizing and tagging digital images for easier retrieval

Enhancing security systems with facial recognition

Enabling autonomous vehicle navigation

Analyzing medical images for diagnostic purposes

Monitoring retail spaces for inventory control

Processing satellite imagery for environmental monitoring

Image/visual generation

Data pipelines are designed to support the generation process when generative models are used to create new images or visual content, such as in graphic design or virtual reality. 

Examples of how image/visual generation drives data pipelines:

Creating virtual models for fashion design

Generating realistic game environments and characters

Simulating architectural visualizations for construction planning

Producing visual content for marketing and advertising

Developing educational tools with custom illustrations

Enhancing film and video production with CGI effects

Creating personalized avatars for social media platforms

Recommender systems

Recommender systems are useful in a wide variety of applications, from e-commerce to content streaming services, where personalized suggestions improve user experience and engagement. 

Examples of how recommender systems drive data pipelines:

Personalizing content recommendations on streaming platforms

Suggesting products to users on e-commerce sites

Tailoring news feeds on social media

Recommending music based on listening habits

Suggesting connections on professional networks

Customizing advertising to user preferences

Proposing travel destinations and activities based on past behavior

[iknowledgenile-blog]

AWS Data Pipeline vs. Amazon Kinesis: Choosing the Right Tool for Your Data Needs

Struggling to decide between AWS Data Pipeline and Amazon Kinesis for your data processing needs? Dive into this quick comparison to understand how each service stacks up. AWS Data Pipeline offers a progressive, precise scheduling approach for batch jobs and reporting, while Amazon Kinesis excels at streaming real-time data for immediate insights Whether you’re looking to consume quick implementation, cost optimization, or real-time analytics, this guide will help you find the right one for your business .

[ryanwilliamsonstuff]

Building Robust Data Pipelines: Best Practices for Seamless Integration

Learn the best practices for building and managing scalable data pipelines. Ensure seamless data flow, real-time analytics, and better decision-making with optimized pipelines.

[johnsongray22]

Stages of the Data Pipeline Development Process

Data pipeline development is a complex and challenging process that requires a systematic approach. Read the blog to understand what are the steps you should follow to build a successful data pipeline.

[phonegap]

Explore the fundamentals of ETL pipelines, focusing on data extraction, transformation, and loading processes. Understand how these stages work together to streamline data integration and enhance organisational insights.

Know more at: https://bit.ly/3xOGX5u

[123albert]

Embark on a journey through the essential components of a data pipeline in our latest blog. Discover the building blocks that lay the foundation for an efficient and high-performance data flow.

[prollcmatchdata]

Data pipeline| Match Data Pro LLC

Maximize efficiency with Match Data Pro LLC's automated data pipeline solutions. Seamlessly configure and execute end-to-end workflows for profiling, cleansing, entity resolution, and fuzzy matching. Trigger jobs manually, via API, or on a schedule with real-time notifications and email confirmations.

Data pipeline

[lisakeller22]

Creating A Successful Data Pipeline: An In-Depth Guide

Explore the blog to learn how to develop a data pipeline for effective data management. Discover the key components, best practices, applications, hurdles, and solutions to streamline processes.

[garymdm]

The Power of Automated Data Lineage: Validating Data Pipelines with Confidence

Introduction In today’s data-driven world, organizations rely on data pipelines to collect, process, and deliver data for crucial business decisions. However, ensuring the accuracy and reliability of these pipelines can be a daunting task. This is where automated data lineage comes into play, offering a solution to validate data pipelines with confidence. In this blog post, we will explore the…

[jcmarchi]

Communications user terminal developed by MIT Lincoln Laboratory prepares for historic moon flyby

New Post has been published on https://thedigitalinsider.com/communications-user-terminal-developed-by-mit-lincoln-laboratory-prepares-for-historic-moon-flyby/

Communications user terminal developed by MIT Lincoln Laboratory prepares for historic moon flyby

In 1969, Apollo 11 astronaut Neil Armstrong stepped onto the moon’s surface — a momentous engineering and science feat marked by his iconic words, “That’s one small step for a man, one giant leap for mankind.” Three years later, Apollo 17 became NASA’s final Apollo mission to land humans on the brightest and largest object in our night sky. Since then, no humans have visited the moon or traveled past low Earth orbit (LEO), largely because of shifting politics, funding, and priorities.

But that is about to change. Through NASA’s Artemis II mission, scheduled to launch no earlier than September 2025, four astronauts will be the first humans to travel to the moon in more than 50 years. In 2022, the uncrewed Artemis I mission proved the ability of NASA’s new spacecraft Orion — launched on the new heavy-lift rocket, the Space Launch System — to travel farther into space than ever before and return safely to Earth. Building on that success, the 10-day Artemis II mission will pave the way for Artemis III, which aims to land astronauts on the lunar surface, with the goal of establishing a future lasting human presence on the moon and preparing for human missions to Mars.

One big step for lasercom

Artemis II will be historic not only for renewing human exploration beyond Earth, but also for being the first crewed lunar flight to demonstrate laser communication (lasercom) technologies, which are poised to revolutionize how spacecraft communicate. Researchers at MIT Lincoln Laboratory have been developing such technologies for more than two decades, and NASA has been infusing them into its missions to meet the growing demands of long-distance and data-intensive space exploration.

As spacecraft push farther into deep space and advanced science instruments collect ultrahigh-definition (HD) data like 4K video and images, missions need better ways to transmit data back to Earth. Communication systems that encode data onto infrared laser light instead of radio waves can send more information at once and be packaged more compactly while operating with less power. Greater volumes of data fuel additional discoveries, and size and power efficiency translate to increased space for science instruments or crew, less expensive launches, and longer-lasting spacecraft batteries.

For Artemis II, the Orion Artemis II Optical Communications System (O2O) will send high-resolution video and images of the lunar surface down to Earth — a stark contrast to the blurry, grainy footage from the Apollo program. In addition, O2O will send and receive procedures, data files, flight plans, voice calls, and other communications, serving as a high-speed data pipeline between the astronauts on Orion and mission control on Earth. O2O will beam information via lasers at up to 260 megabits per second (Mbps) to ground optical stations in one of two NASA locations: the White Sands Test Facility in Las Cruces, New Mexico, or the Jet Propulsion Laboratory’s Table Mountain Facility in Wrightwood, California. Both locations are ideal for their minimal cloud coverage, which can obstruct laser signals as they enter Earth’s atmosphere.

At the heart of O2O is the Lincoln Laboratory–developed Modular, Agile, Scalable Optical Terminal (MAScOT). About the size of a house cat, MAScOT features a 4-inch telescope mounted on a two-axis pivoted support (gimbal), and fixed back-end optics. The gimbal precisely points the telescope and tracks the laser beam through which communications signals are emitted and received, in the direction of the desired data recipient or sender. Underneath the gimbal, in a separate assembly, are the back-end optics, which contain light-focusing lenses, tracking sensors, fast-steering mirrors, and other components to finely point the laser beam.

A series of firsts

MAScOT made its debut in space as part of the laboratory’s Integrated Laser Communications Relay Demonstration (LCRD) LEO User Modem and Amplifier Terminal (ILLUMA-T), which launched to the International Space Station (ISS) in November 2023. After a few weeks of preliminary testing, ILLUMA-T transmitted its first beam of laser light to NASA’s LCRD satellite in geosynchronous (GEO) orbit 22,000 miles above Earth’s surface. Achieving this critical step, known as “first light,” required precise pointing, acquisition, and tracking of laser beams between moving spacecraft.

Over the following six months, the laboratory team performed experiments to test and characterize the system’s basic functionality, performance, and utility for human crews and user applications. Initially, the team checked whether the ILLUMA-T-to-LCRD optical link was operating at the intended data rates in both directions: 622 Mbps down and 51 Mbps up. In fact, even higher data rates were achieved: 1.2 gigabits per second down and 155 Mbps up.  

“This first demonstration of a two-way, end-to-end laser communications relay system, in which ILLUMA-T was the first LEO user of LCRD, is a major milestone for NASA and other space organizations,” says Bryan Robinson, leader of the laboratory’s Optical and Quantum Communications Group. “It serves as a precursor to optical relays at the moon and Mars.”

After the relay was up and running, the team assessed how parameters such as laser transmit power, optical wavelength, and relative sun angles impact terminal performance. Lastly, they contributed to several networking experiments over multiple nodes to and from the ISS, using NASA’s delay/disruption tolerant networking protocols. One landmark experiment streamed 4K video on a round-trip journey from an airplane flying over Lake Erie in Ohio, to the NASA Glenn Research Center in nearby Cleveland, to the NASA White Sands Test Facility in New Mexico, to LCRD in GEO, to ILLUMA-T on the ISS, and then back. In June 2024, ILLUMA-T communicated with LCRD for the last time and powered off.

“Our success with ILLUMA-T lays the foundation for streaming HD video to and from the moon,” says co-principal investigator Jade Wang, an assistant leader of the Optical and Quantum Communications Group. “You can imagine the Artemis astronauts using videoconferencing to connect with physicians, coordinate mission activities, and livestream their lunar trips.”

Moon ready

The Artemis II O2O mission will employ the same overall MAScOT design proven on ILLUMA-T. Lincoln Laboratory delivered the payload to NASA’s Kennedy Space Center for installation and testing on the Orion spacecraft in July 2023.

“Technology transfer to government is what Lincoln Laboratory does as a federally funded research and development center,” explains lead systems engineer Farzana Khatri, a senior staff member in the Optical and Quantum Communications Group. “We not only transfer technology, but also work with our transfer partner to ensure success. To prepare for O2O, we are leveraging lessons learned during ILLUMA-T operations. Recently, we conducted pre-mission dry runs to enhance coordination among the various teams involved.”

In August 2024, the laboratory completed an important milestone for the O2O optical terminal: the mission readiness test. The test involved three phases. In the first phase, they validated terminal command and telemetry functions. While laboratory-developed ground software was directly used to command and control ILLUMA-T, for O2O, it will run in the background and all commands and telemetry will be interfaced through software developed by NASA’s Johnson Space Center Mission Control Center. In the second phase, the team tested different user applications, including activating some of Orion’s HD cameras and sending videos from Cape Canaveral to Johnson Space Center as a mock-up for the actual space link. They also ran file transfers, video conferencing, and other operations on astronaut personal computing devices. In the third phase, they simulated payload commissioning activities, such as popping the latch on the optical hardware and moving the gimbal, and conducting ground terminal operations.

“For O2O, we want to show that this optical link works and is helpful to astronauts and the mission,” Khatri says. “The Orion spacecraft collects a huge amount of data within the first day of a mission, and typically these data sit on the spacecraft until it lands and take months to be offloaded. With an optical link running at the highest rate, we should be able to get data down to Earth within a few hours for immediate analysis. Furthermore, astronauts can stay in touch with Earth during their journey, inspiring the public and the next generation of deep-space explorers, much like the Apollo 11 astronauts who first landed on the moon 55 years ago.”

O2O is funded by the Space Communication and Navigation program at NASA Headquarters in Washington. O2O was developed by a team of engineers from NASA’s Goddard Space Flight Center and Lincoln Laboratory. This collaboration has led to multiple lasercom missions, such as the 2013 Lunar Laser Communication Demonstration, the 2021 LCRD, the 2022 TeraByte Infrared Delivery, and the 2023 ILLUMA-T.

[rajaniesh]

Maximize Efficiency with Volumes in Databricks Unity Catalog

With Databricks Unity Catalog's volumes feature, managing data has become a breeze. Regardless of the format or location, the organization can now effortlessly access and organize its data. This newfound simplicity and organization streamline data managem