Smart Data Collective

SEPTEMBER 23, 2020

Data warehouse needs a lower level of knowledge or skill in data science and programming to use. Engineers set up and maintained data lakes, and they include them into the data pipeline. Data scientists also work closely with data lakes because they have information on a broader as well as current scope.

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Women in Big Data

OCTOBER 9, 2024

These procedures are central to effective data management and crucial for deploying machine learning models and making data-driven decisions. The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline?

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

Dataconomy

MARCH 19, 2025

Data science team composition A well-rounded data science team comprises various roles that contribute to its success. Roles within a data science team Data engineer: Responsible for building and managing data pipelines. Data visualization developer: Creates interactive dashboards for data analysis.

Data Science 91

Data Science Citizen Data Scientist Data Scientist Machine Learning 91

Pickl AI

APRIL 21, 2025

Key Takeaways Big Data focuses on collecting, storing, and managing massive datasets. Data Science extracts insights and builds predictive models from processed data. Big Data technologies include Hadoop, Spark, and NoSQL databases. Data Science uses Python, R, and machine learning frameworks.

Big Data 52

Big Data Big Data Data Science Machine Learning 52

Pickl AI

DECEMBER 9, 2024

First, lets understand the basics of Big Data. Key Takeaways Understand the 5Vs of Big Data: Volume, Velocity, Variety, Veracity, Value. Familiarise yourself with essential tools like Hadoop and Spark. Practice coding skills in languages relevant to Big Data roles. What are the Main Components of Hadoop?

Big Data 52

Big Data Big Data Hadoop Apache Kafka 52

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 Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

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. Big Data Technologies: Hadoop, Spark, etc.

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Data Engineering Data Engineer Data Engineering Data Engineering 52

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.

Data Scientist 96

Data Scientist Machine Learning Machine Learning Computer Science 96

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

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Data Engineering Data Engineering Data Engineer Data Engineering 52

Alation

FEBRUARY 20, 2020

Alation’s deep integration with tools like MicroStrategy and Tableau provides visibility into the complete data pipeline: from storage through visualization. Many of our customers have been telling us that these two tools in particular form the core of their visual analytics environments.

Data Governance 52

Data Governance Tableau Hadoop Data Pipeline 52

phData

FEBRUARY 25, 2025

Its agent-based data replication ensures that it works with both on-prem and cloud-hosted source systems, providing a fault-tolerant, scalable solution for data integration. Furthermore, Datavolo provides a graphical UI that simplifies defining data pipelines.

Data Pipeline 52

Data Pipeline ETL Data Engineering Data Engineer 52

O'Reilly Media

SEPTEMBER 15, 2021

A platform, clearly, but a platform for building data pipelines that’s qualitatively different from a platform like Ray, Spark, or Hadoop. In 2021, Hadoop often seems like legacy software, but 15% of the respondents were working on the Hadoop platform, with an average salary of $166,000. What about Kafka?

AI 145

AI AI Azure AWS 145

Data Science Dojo

JULY 3, 2024

Big Data Technologies : Handling and processing large datasets using tools like Hadoop, Spark, and cloud platforms such as AWS and Google Cloud. Data Processing and Analysis : Techniques for data cleaning, manipulation, and analysis using libraries such as Pandas and Numpy in Python.

Data Science 150

Data Science Machine Learning Machine Learning Data Visualization 150

Pickl AI

JUNE 7, 2024

IBM Infosphere DataStage IBM Infosphere DataStage is an enterprise-level ETL tool that enables users to design, develop, and run data pipelines. Key Features: Graphical Framework: Allows users to design data pipelines with ease using a graphical user interface. Read More: Advanced SQL Tips and Tricks for Data Analysts.

ETL 40

ETL Data Quality Data Pipeline Data Warehouse 40

DataRobot Blog

OCTOBER 3, 2017

“Having information in one place – from first-party data, to second- and third-party data – has made every additional use case an incremental add-on,” he said, emphasizing that being modular helped them to avoid creating data pipelines for every use case. “We 3) Data professionals come in all shapes and forms.

Data Lakes 52

Data Lakes Azure Data Pipeline Hadoop 52

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data Science 94

Data Science Analytics Analytics Data Scientist 94

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 Engineering Data Engineer 91

DataSeries

AUGUST 15, 2024

Data Engineering Career: Unleashing The True Potential of Data Problem-Solving Skills Data Engineers are required to possess strong analytical and problem-solving skills to navigate complex data challenges. Hadoop, Spark).

Data Engineering 40

Data Engineering Data Engineering Data Engineer Data Engineering 40

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.

AWS 52

AWS Cloud Computing Data Pipeline Big Data 52

ODSC - Open Data Science

OCTOBER 7, 2024

Keeping track of changes in data, model parameters, and infrastructure configurations is essential for reliable AI development, ensuring models can be rebuilt and improved efficiently. Building Scalable Data Pipelines The foundation of any AI pipeline is the data it consumes.

Machine Learning 52

Machine Learning Machine Learning AI AI 52

IBM Journey to AI blog

AUGUST 29, 2024

Flink jobs, designed to process continuous data streams, are key to making this possible. How Apache Flink enhances real-time event-driven businesses Imagine a retail company that can instantly adjust its inventory based on real-time sales data pipelines.

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Apache Kafka Hadoop ETL Data Pipeline 45

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. It provides ACID transactions, scalable metadata management, and schema enforcement to data lakes.

Machine Learning 98

Machine Learning Machine Learning Data Lakes Data Science 98

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.

Machine Learning 52

Machine Learning Machine Learning Data Lakes AI 52

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

DECEMBER 26, 2024

Key data sources include social media platforms, web analytics tools, customer feedback systems, and IoT devices, all of which contribute to a rich tapestry of actionable insights. Role of Analytics Tools in Big Data Analytics tools like Hadoop , Tableau , and predictive platforms make Big Data manageable.

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Big Data Big Data Big Data Analytics Big Data Analytics 52

The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams.

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ML ML Data Lakes Machine Learning 52

phData

FEBRUARY 5, 2024

The difference view compares all the data collected in scans and profiles: This single visual diff can replace hours and hours of manual checks and end users complaining about missing data or, in the worst case, making a decision based on bad data. Get to know all the ins and outs of your upcoming migration.

SQL 52

SQL Database Data Quality Data Warehouse 52

DagsHub

AUGUST 23, 2024

It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines. It allows users to define, measure, monitor, and validate data quality.

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 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

APRIL 26, 2023

As models become more complex and the needs of the organization evolve and demand greater predictive abilities, you’ll also find that machine learning engineers use specialized tools such as Hadoop and Apache Spark for large-scale data processing and distributed computing.

Data Analyst 52

Data Analyst Machine Learning Machine Learning Power BI 52

ODSC - Open Data Science

JANUARY 7, 2025

Data Engineering Data engineering remains integral to many data science roles, with workflow pipelines being a key focus. Tools like Apache Airflow are widely used for scheduling and monitoring workflows, while Apache Spark dominates big data pipelines due to its speed and scalability.

Data Scientist 40

Data Scientist Data Science Machine Learning Machine Learning 40

phData

OCTOBER 20, 2023

Here is an example of a simple XML document: 1 Scientists 1 Mike Bills Jr Scientist 234 Octopus Avenue Stamford CT 60429 2000-05-01 2000-12-01 Parquet Parquet is a file format for storing big data in a columnar storage format. It is specifically designed to work seamlessly with Hadoop and other big data processing frameworks.

Big Data 40

Big Data Big Data Database Hadoop 40

Alation

FEBRUARY 20, 2020

With Alation, you can search for assets across the entire data pipeline. Alation catalogs and crawls all of your data assets, whether it is in a traditional relational data set (MySQL, Oracle, etc), a SQL on Hadoop system (Presto, SparkSQL,etc), a BI visualization or something in a file system, such as HDFS or AWS S3.

SQL 52

SQL Hadoop Analytics Analytics 52