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?

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Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

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ODSC - Open Data Science

SEPTEMBER 29, 2023

Data management problems can also lead to data silos; disparate collections of databases that don’t communicate with each other, leading to flawed analysis based on incomplete or incorrect datasets. One way to address this is to implement a data lake: a large and complex database of diverse datasets all stored in their original format.

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Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 101

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The MLOps Blog

MAY 17, 2023

We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one. What is an ETL data pipeline in ML? Xoriant It is common to use ETL data pipeline and data pipeline interchangeably.

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ODSC - Open Data Science

OCTOBER 13, 2024

Be sure to check out her talk, “ Don’t Go Over the Deep End: Building an Effective OSS Management Layer for Your Data Lake ,” there! Managing a data lake can often feel like being lost at sea — especially when dealing with both structured and unstructured data.

Data Lakes 40

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AWS Machine Learning Blog

AUGUST 8, 2024

Managing and retrieving the right information can be complex, especially for data analysts working with large data lakes and complex SQL queries. This post highlights how Twilio enabled natural language-driven data exploration of business intelligence (BI) data with RAG and Amazon Bedrock.

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Smart Data Collective

AUGUST 17, 2022

You can safely use an Apache Kafka cluster for seamless data movement from the on-premise hardware solution to the data lake using various cloud services like Amazon’s S3 and others. It will enable you to quickly transform and load the data results into Amazon S3 data lakes or JDBC data stores.

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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

Women in Big Data

OCTOBER 9, 2024

The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline? A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake.

Big Data 59

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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. This can also make the learning process challenging.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Database 98

Tableau

JUNE 8, 2021

Domain experts, for example, feel they are still overly reliant on core IT to access the data assets they need to make effective business decisions. In all of these conversations there is a sense of inertia: Data warehouses and data lakes feel cumbersome and data pipelines just aren't agile enough.

Tableau 52

Tableau Data Lakes Data Warehouse SQL 52

Smart Data Collective

OCTOBER 27, 2021

Druid is specifically designed to support workflows that require fast ad-hoc analytics, concurrency, and instant data visibility are core necessities. It is easy to integrate with any existing data pipelines, and it can also stream data from the most popular message buses such as Amazon Kinesis and Kafka.

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ODSC - Open Data Science

JANUARY 30, 2024

Not only does it involve the process of collecting, storing, and processing data so that it can be used for analysis and decision-making, but these professionals are responsible for building and maintaining the infrastructure that makes this possible; and so much more. Think of data engineers as the architects of the data ecosystem.

Data Engineer 52

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Dataversity

JANUARY 25, 2023

Over the past few years, enterprise data architectures have evolved significantly to accommodate the changing data requirements of modern businesses. Data warehouses were first introduced in the […] The post Are Data Warehouses Still Relevant?

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Data Warehouse Data Lakes Cloud Computing Data Pipeline 52

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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Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. They are crucial in ensuring data is readily available for analysis and reporting.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Tableau

DECEMBER 7, 2022

Every company today is being asked to do more with less, and leaders need access to fresh, trusted KPIs and data-driven insights to manage their businesses, keep ahead of the competition, and provide unparalleled customer experiences. . But good data—and actionable insights—are hard to get.

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Tableau

DECEMBER 7, 2022

Every company today is being asked to do more with less, and leaders need access to fresh, trusted KPIs and data-driven insights to manage their businesses, keep ahead of the competition, and provide unparalleled customer experiences. . But good data—and actionable insights—are hard to get.

Tableau 98

Tableau Data Warehouse Data Pipeline Data Visualization 98

The MLOps Blog

JUNE 27, 2023

LakeFS LakeFS is an open-source platform that provides data lake versioning and management capabilities. It sits between the data lake and cloud object storage, allowing you to version and control changes to data lakes at scale. Flyte Flyte is a platform for orchestrating ML pipelines at scale.

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The MLOps Blog

JANUARY 23, 2023

These tools may have their own versioning system, which can be difficult to integrate with a broader data version control system. For instance, our data lake could contain a variety of relational and non-relational databases, files in different formats, and data stored using different cloud providers. DVC Git LFS neptune.ai

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ML ML Data Lakes Machine Learning 52

AWS Machine Learning Blog

JUNE 18, 2024

The output data is transformed to a standardized format and stored in a single location in Amazon S3 in Parquet format, a columnar and efficient storage format. With AWS Glue custom connectors, it’s effortless to transfer data between Amazon S3 and other applications.

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Women in Big Data

NOVEMBER 27, 2024

It features Synapse Studio, a collaborative workspace for data integration, exploration, and analysis, allowing users to manage data pipelines seamlessly. architecture for both structured and unstructured data.

Data Warehouse 59

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ODSC - Open Data Science

FEBRUARY 14, 2023

It also addresses the strategies and best practices for implementing a data mesh. Applying Engineering Best Practices in Data Lakes Architectures Einat Orr | Ceo and Co-Founder | Treeverse This talk examines why agile methodology, continuous integration, and continuous deployment and production monitoring are essential for data lakes.

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Flipboard

AUGUST 17, 2023

Amazon Redshift uses SQL to analyze structured and semi-structured data across data warehouses, operational databases, and data lakes, using AWS-designed hardware and ML to deliver the best price-performance at any scale. If you want to do the process in a low-code/no-code way, you can follow option C.

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ODSC - Open Data Science

FEBRUARY 24, 2023

Great Expectations provides support for different data backends such as flat file formats, SQL databases, Pandas dataframes and Sparks, and comes with built-in notification and data documentation functionality. You can watch it on demand here.

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ODSC - Open Data Science

OCTOBER 15, 2023

Cloudera Cloudera is a cloud-based platform that provides businesses with the tools they need to manage and analyze data. They offer a variety of services, including data warehousing, data lakes, and machine learning. The platform includes several features that make it easy to develop and test data pipelines.

Machine Learning 52

Machine Learning Machine Learning Data Pipeline AI 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

By analyzing datasets, data scientists can better understand their potential use in an algorithm or machine learning model. The data science lifecycle Data science is iterative, meaning data scientists form hypotheses and experiment to see if a desired outcome can be achieved using available data.

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ODSC - Open Data Science

FEBRUARY 15, 2024

These systems represent data as knowledge graphs and implement graph traversal algorithms to help find content in massive datasets. These systems are not only useful for a wide range of industries, they are fun for data engineers to work on.

Data Engineer 52

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Pickl AI

JULY 30, 2024

Flow-Based Programming : NiFi employs a flow-based programming model, allowing users to create complex data flows using simple drag-and-drop operations. This visual representation simplifies the design and management of data pipelines.

ETL 52

ETL Data Lakes Big Data Big Data 52

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

Data Engineer 52

Data Engineer Data Engineering Data Engineering Data Engineering 52

Alation

MARCH 22, 2022

They created each capability as modules, which can either be used independently or together to build automated data pipelines. The table details are extracted from the IDF pipeline information, which then syncs details like column, table, business, and technical metadata. How the IDF Supports a Smarter Data Pipeline.

DataOps 52

DataOps Data Pipeline Data Engineering Data Engineer 52

phData

APRIL 29, 2024

Oracle – The Oracle connector, a database-type connector, enables real-time data transfer of large volumes of data from on-premises or cloud sources to the destination of choice, such as a cloud data lake or data warehouse.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

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.

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Dataconomy

FEBRUARY 23, 2023

This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified. Creating data pipelines and workflows Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineer Data Engineering 91

DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

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