Data Science Dojo

FEBRUARY 20, 2023

It is used by businesses across industries for a wide range of applications, including fraud prevention, marketing automation, customer service, artificial intelligence (AI), chatbots, virtual assistants, and recommendations. we have Databricks which is an open-source, next-generation data management platform.

Machine Learning 195

Machine Learning Machine Learning AWS Azure 195

IBM Journey to AI blog

OCTOBER 22, 2024

Data is the differentiator as business leaders look to utilize their competitive edge as they implement generative AI (gen AI). Leaders feel the pressure to infuse their processes with artificial intelligence (AI) and are looking for ways to harness the insights in their data platforms to fuel this movement.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

Alation

MAY 16, 2023

But with the sheer amount of data continually increasing, how can a business make sense of it? Robust data pipelines. What is a Data Pipeline? A data pipeline is a series of processing steps that move data from its source to its destination. The answer?

Data Pipeline 40

Data Pipeline Data Governance Data Lakes Data Warehouse 40

Precisely

FEBRUARY 20, 2025

Follow five essential steps for success in making your data AI ready with data integration. Define clear goals, assess your data landscape, choose the right tools, ensure data quality and governance, and continuously optimize your integration processes.

Data Silos 69

Data Silos AI AI Data Quality 69

Applied Data Science

AUGUST 2, 2021

Automation Automating data pipelines and models ➡️ 6. The Data Engineer Not everyone working on a data science project is a data scientist. Data engineers are the glue that binds the products of data scientists into a coherent and robust data pipeline.

Data Science 130

Data Science Data Scientist ML ML 130

IBM Journey to AI blog

MAY 20, 2024

Implement data lineage tooling and methodologies: Tools are available that help organizations track the lineage of their data sets from ultimate source to target by parsing code, ETL (extract, transform, load) solutions and more. Your data scientists, executives and customers will thank you!

Machine Learning 110

Machine Learning Machine Learning Data Scientist ML 110

AWS Machine Learning Blog

JUNE 18, 2024

More than 170 tech teams used the latest cloud, machine learning and artificial intelligence technologies to build 33 solutions. LLMs excel at writing code and reasoning over text, but tend to not perform as well when interacting directly with time-series data.

AWS 119

AWS AI AI Python 119

Data Science Dojo

JULY 3, 2024

Data Engineering : Building and maintaining data pipelines, ETL (Extract, Transform, Load) processes, and data warehousing. Artificial Intelligence : Concepts of AI include neural networks, natural language processing (NLP), and reinforcement learning.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

ODSC - Open Data Science

JUNE 19, 2023

There is no doubt that real-time operating systems (RTOS) have an important role in the future of big data collection and processing. How does RTOS help advance big data processing? Advanced analytics and AI  — It is virtually impossible to extract insights from big data through conventional evaluation and analysis, let alone manually.

Big Data 96

Big Data Big Data Artificial Intelligence Artificial Intelligence 96

Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Warehousing: Amazon Redshift, Google BigQuery, etc.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

AWS Machine Learning Blog

FEBRUARY 21, 2025

Previously, he was a Data & Machine Learning Engineer at AWS, where he worked closely with customers to develop enterprise-scale data infrastructure, including data lakes, analytics dashboards, and ETL pipelines. He specializes in designing, building, and optimizing large-scale data solutions.

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AWS Natural Language Processing Machine Learning Machine Learning 111

IBM Journey to AI blog

MAY 9, 2024

How can a healthcare provider improve its data governance strategy, especially considering the ripple effect of small changes? Data lineage can help.With data lineage, your team establishes a strong data governance strategy, enabling them to gain full control of your healthcare data pipeline.

Data Governance 85

Data Governance Data Silos Data Quality Predictive Analytics 85

IBM Journey to AI blog

APRIL 20, 2023

Read this e-book on building strong governance foundations Why automated data lineage is crucial for success Data lineage , the process of tracking the flow of data over time from origin to destination within a data pipeline, is essential to understand the full lifecycle of data and ensure regulatory compliance.

Database 86

Database Data Engineering Data Engineering Data Engineering 86

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

ODSC - Open Data Science

OCTOBER 18, 2024

Set specific, measurable targets Data science goals to “increase sales” lack the clarity needed to evaluate success and secure ongoing funding. Audit existing data assets Inventory internal datasets, ETL capabilities, past analytical initiatives, and available skill sets.

Data Science 52

Data Science Data Scientist Analytics Analytics 52

Alation

JANUARY 17, 2023

It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse. Data ingestion/integration services. Reverse ETL tools. Data orchestration tools. Business intelligence (BI) platforms.

Data Warehouse 52

Data Warehouse ETL Tableau Cloud Data 52

phData

SEPTEMBER 25, 2024

With the importance of data in various applications, there’s a need for effective solutions to organize, manage, and transfer data between systems with minimal complexity. While numerous ETL tools are available on the market, selecting the right one can be challenging.

ETL 52

ETL Database Data Warehouse Analytics 52

phData

APRIL 18, 2023

Data movements lead to high costs of ETL and rising data management TCO. The inability to access and onboard new datasets prolong the data pipeline’s creation and time to market. Data co-location enables teams to access, join, query, and analyze internal and external vendor data with minimal to no ETL.

Data Silos 52

Data Silos ETL Clustering Analytics 52

Precisely

OCTOBER 3, 2024

Companies once relied heavily on on-premises ETL and data lakes, but today, there’s a shift towards cloud-native data environments. The business case: The broadcasting company had an existing on-prem data management stack, with data pipelines operating on delays of up to 10 hours.

AWS 64

AWS Data Lakes ETL Data Pipeline 64

Precisely

MAY 23, 2024

The sudden popularity of cloud data platforms like Databricks , Snowflake , Amazon Redshift, Amazon RDS, Confluent Cloud , and Azure Synapse has accelerated the need for powerful data integration tools that can deliver large volumes of information from transactional applications to the cloud reliably, at scale, and in real time.

Data Governance 64

Data Governance ETL Data Pipeline Azure 64

Pickl AI

JULY 25, 2024

Improved Decision-making By providing a consolidated and accessible view of data, organisations can identify trends, patterns, and anomalies more quickly, leading to better-informed and timely decisions. Data Ingestion Tools To facilitate the process, various tools and technologies are available. The post What is Data Ingestion?

Apache Kafka 52

Apache Kafka Data Lakes Data Warehouse Data Quality 52

Precisely

MAY 23, 2024

The sudden popularity of cloud data platforms like Databricks , Snowflake , Amazon Redshift, Amazon RDS, Confluent Cloud , and Azure Synapse has accelerated the need for powerful data integration tools that can deliver large volumes of information from transactional applications to the cloud reliably, at scale, and in real time.

Data Governance 52

Data Governance ETL Data Pipeline Azure 52

ODSC - Open Data Science

APRIL 4, 2024

Find out how to weave data reliability and quality checks into the execution of your data pipelines and more. More Speakers and Sessions Announced for the 2024 Data Engineering Summit Ranging from experimentation platforms to enhanced ETL models and more, here are some more sessions coming to the 2024 Data Engineering Summit.

Data Visualization 52

Data Visualization Analytics Analytics Big Data Analytics 52

Pickl AI

OCTOBER 10, 2024

It integrates well with cloud services, databases, and big data platforms like Hadoop, making it suitable for various data environments. Typical use cases include ETL (Extract, Transform, Load) tasks, data quality enhancement, and data governance across various industries.

Data Quality 52

Data Quality AWS Machine Learning Machine Learning 52

ODSC - Open Data Science

SEPTEMBER 29, 2023

It truly is an all-in-one data lake solution. HPCC Systems and Spark also differ in that they work with distinct parts of the big data pipeline. Spark is more focused on data science, ingestion, and ETL, while HPCC Systems focuses on ETL and data delivery and governance.

Data Lakes 52

Data Lakes Clustering Big Data Big Data 52

IBM Journey to AI blog

JULY 17, 2023

The next generation of Db2 Warehouse SaaS and Netezza SaaS on AWS fully support open formats such as Parquet and Iceberg table format, enabling the seamless combination and sharing of data in watsonx.data without the need for duplication or additional ETL.

AI 74

AI AI Machine Learning Machine Learning 74

phData

JUNE 7, 2023

The most critical and impactful step you can take towards enterprise AI today is ensuring you have a solid data foundation built on the modern data stack with mature operational pipelines, including all your most critical operational data. It can learn, reason, and adapt to new situations like humans.

AI 59

AI AI Machine Learning Machine Learning 59

Dataconomy

FEBRUARY 12, 2024

The acronym ETL—Extract, Transform, Load—has long been the linchpin of modern data management, orchestrating the movement and manipulation of data across systems and databases. This methodology has been pivotal in data warehousing, setting the stage for analysis and informed decision-making.

ETL 195

ETL Data Governance Machine Learning Machine Learning 195

Flipboard

NOVEMBER 24, 2023

SnapLogic’s AI journey In the realm of integration platforms, SnapLogic has consistently been at the forefront, harnessing the transformative power of artificial intelligence. Iris was designed to use machine learning (ML) algorithms to predict the next steps in building a data pipeline.

Database 158

Database AWS ETL SQL 158

AWS Machine Learning Blog

SEPTEMBER 18, 2024

The ZMP analyzes billions of structured and unstructured data points to predict consumer intent by using sophisticated artificial intelligence (AI) to personalize experiences at scale. Though it’s worth mentioning that Airflow isn’t used at runtime as is usual for extract, transform, and load (ETL) tasks.

AWS 112

AWS Machine Learning Machine Learning ML 112

phData

JULY 18, 2023

Slow Response to New Information: Legacy data systems often lack the computation power necessary to run efficiently and can be cost-inefficient to scale. This typically results in long-running ETL pipelines that cause decisions to be made on stale or old data.

Data Warehouse 52

Data Warehouse Analytics Analytics Cloud Data 52

Dataconomy

FEBRUARY 25, 2025

This guide offers a strategic pathway to implementing data systems that not only support current needs but are adaptable to future technological advancements. The evolution of artificial intelligence (AI) has highlighted the critical need for AI-ready data systems within modern enterprises.

Data Pipeline 103

Data Pipeline AI AI ETL 103

ODSC - Open Data Science

DECEMBER 9, 2024

Were talking automated data cleaning, ETL pipeline generation, feature selection for models, hyperparameter tuningremoving grunt work to free up analyst time/energy for higher thinking. The most skilled data scientists may leverage these starting-point recommendations to boost productivity.

Data Science 40

Data Science Machine Learning Machine Learning Python 40

ODSC - Open Data Science

MARCH 12, 2025

The explosion of generative AI and LLMs has redefined how businesses and developers interact with artificial intelligence. Data Engineerings SteadyGrowth 20182021: Data engineering was often mentioned but overshadowed by modeling advancements.

Data Science 52

Data Science Machine Learning Machine Learning Data Engineer 52

IBM Journey to AI blog

AUGUST 4, 2023

It’s distributed both in the cloud and on-premises, allowing extensive use and movement across clouds, apps and networks, as well as stores of data at rest. An architecture designed for data democratization aims to be flexible, integrated, agile and secure to enable the use of data and artificial intelligence (AI) at scale.

Data Lakes 73

Data Lakes AI AI Data Governance 73