Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

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The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

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ODSC - Open Data Science

DECEMBER 19, 2023

Data Engineer Data engineers are responsible for the end-to-end process of collecting, storing, and processing data. They use their knowledge of data warehousing, data lakes, and big data technologies to build and maintain data pipelines.

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ODSC - Open Data Science

FEBRUARY 2, 2023

Big Data As datasets become larger and more complex, knowing how to work with them will be key. Big data isn’t an abstract concept anymore, as so much data comes from social media, healthcare data, and customer records, so knowing how to parse all of that is needed.

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Dataconomy

FEBRUARY 23, 2023

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. They must also ensure that data privacy regulations, such as GDPR and CCPA , are followed.

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IBM Journey to AI blog

JANUARY 5, 2023

The first generation of data architectures represented by enterprise data warehouse and business intelligence platforms were characterized by thousands of ETL jobs, tables, and reports that only a small group of specialized data engineers understood, resulting in an under-realized positive impact on the business.

Data Quality 52

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Precisely

JANUARY 19, 2023

Organizations that can master the challenges of data integration, data quality, and context will be well positioned to identify opportunities and threats quickly, and then to take decisive action to gain competitive advantage. Containerization Docker containers are revolutionizing the way organizations host and deply applications.

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Smart Data Collective

OCTOBER 17, 2022

Big data is shaping our world in countless ways. Data powers everything we do. Exactly why, the systems have to ensure adequate, accurate and most importantly, consistent data flow between different systems. There are a number of challenges in data storage , which data pipelines can help address.

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IBM Journey to AI blog

SEPTEMBER 19, 2023

To pursue a data science career, you need a deep understanding and expansive knowledge of machine learning and AI. And you should have experience working with big data platforms such as Hadoop or Apache Spark. Your skill set should include the ability to write in the programming languages Python, SAS, R and Scala.

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IBM Journey to AI blog

DECEMBER 16, 2022

For example, data catalogs have evolved to deliver governance capabilities like managing data quality and data privacy and compliance. It uses metadata and data management tools to organize all data assets within your organization. This is especially helpful when handling massive amounts of big data.

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AWS Machine Learning Blog

FEBRUARY 13, 2024

Let’s demystify this using the following personas and a real-world analogy: Data and ML engineers (owners and producers) – They lay the groundwork by feeding data into the feature store Data scientists (consumers) – They extract and utilize this data to craft their models Data engineers serve as architects sketching the initial blueprint.

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DagsHub

DECEMBER 11, 2023

It does not support the ‘dvc repro’ command to reproduce its data pipeline. DVC Released in 2017, Data Version Control ( DVC for short) is an open-source tool created by iterative. However, these tools have functional gaps for more advanced data workflows. Git LFS requires a LFS server to work.

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Machine Learning Machine Learning Data Lakes Database 98

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

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The MLOps Blog

JUNE 27, 2023

Databricks Databricks is a cloud-native platform for big data processing, machine learning, and analytics built using the Data Lakehouse architecture. LakeFS LakeFS is an open-source platform that provides data lake versioning and management capabilities.

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Flipboard

NOVEMBER 24, 2023

JuMa is tightly integrated with a range of BMW Central IT services, including identity and access management, roles and rights management, BMW Cloud Data Hub (BMW’s data lake on AWS) and on-premises databases. Furthermore, the notebooks can be integrated into the corporate Git repositories to collaborate using version control.

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IBM Journey to AI blog

OCTOBER 12, 2023

Securing AI models and their access to data While AI models need flexibility to access data across a hybrid infrastructure, they also need safeguarding from tampering (unintentional or otherwise) and, especially, protected access to data. Bias can also find its way into a model’s outputs long after deployment.

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AI AI Data Scientist Data Governance 83

Alation

APRIL 14, 2022

Data pipeline orchestration. Moving/integrating data in the cloud/data exploration and quality assessment. A cloud environment with such features will support collaboration across departments and across common data types, including csv, JSON, XML, AVRO, Parquet, Hyper, TDE, and more. Collaboration and governance.

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Alation

FEBRUARY 20, 2020

The rise of data lakes, IOT analytics, and big data pipelines has introduced a new world of fast, big data. Now, agility and self-service are favored over batch processing and dependency on IT.

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DataRobot Blog

OCTOBER 3, 2017

With this integration, customers can now harness the full power of Azure’s Big Data offerings in a self-service manner to gain immediate value.”. This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance.

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