Pickl AI

AUGUST 1, 2023

Aspiring and experienced Data Engineers alike can benefit from a curated list of books covering essential concepts and practical techniques. These 10 Best Data Engineering Books for beginners encompass a range of topics, from foundational principles to advanced data processing methods. What is Data Engineering?

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Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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ODSC - Open Data Science

JANUARY 2, 2024

We couldn’t be more excited to announce two events that will be co-located with ODSC East in Boston this April: The Data Engineering Summit and the Ai X Innovation Summit. Data Engineering Summit Our second annual Data Engineering Summit will be in-person for the first time! Learn more about them below.

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Smart Data Collective

OCTOBER 17, 2022

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Explosive data growth can be too much to handle.

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Pickl AI

JULY 25, 2023

Unfolding the difference between data engineer, data scientist, and data analyst. Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. Data Visualization: Matplotlib, Seaborn, Tableau, etc.

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Smart Data Collective

JUNE 10, 2022

Experts in data science are needed in all kinds of industries, from companies developing dating apps to government security. Businesses and organizations of all kinds rely on big data to find solutions to problems and provide better services, so there are lots of different types of careers you could pursue with a degree in data science.

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IBM Data Science in Practice

MARCH 8, 2023

Additionally, imagine being a practitioner, such as a data scientist, data engineer, or machine learning engineer, who will have the daunting task of learning how to use a multitude of different tools. A feature platform should automatically process the data pipelines to calculate that feature.

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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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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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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.

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Data Science Blog

FEBRUARY 4, 2023

If the data sources are additionally expanded to include the machines of production and logistics, much more in-depth analyses for error detection and prevention as well as for optimizing the factory in its dynamic environment become possible.

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AWS Machine Learning Blog

DECEMBER 4, 2024

It seems straightforward at first for batch data, but the engineering gets even more complicated when you need to go from batch data to incorporating real-time and streaming data sources, and from batch inference to real-time serving.

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AWS Machine Learning Blog

AUGUST 8, 2024

The LookML view and model information is brought into Amazon S3 through a separate data pipeline (not shown). About the Authors Apurva Gawad is a Senior Data Engineer at Twilio specializing in building scalable systems for data ingestion and empowering business teams to derive valuable insights from data.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. For example, neptune.ai

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ODSC - Open Data Science

APRIL 4, 2024

Data Analytics in the Age of AI, When to Use RAG, Examples of Data Visualization with D3 and Vega, and ODSC East Selling Out Soon Data Analytics in the Age of AI Let’s explore the multifaceted ways in which AI is revolutionizing data analytics, making it more accessible, efficient, and insightful than ever before.

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Data Visualization Analytics Analytics Big Data Analytics 52

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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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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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. He works closely with enterprise customers to design data platforms and build advanced analytics and ML use cases.

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The MLOps Blog

FEBRUARY 5, 2023

This pipeline facilitates the smooth, automated flow of information, preventing many problems that enterprises face, such as data corruption, conflict, and duplication of data entries. A streaming data pipeline is an enhanced version which is able to handle millions of events in real-time at scale.

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Machine Learning Machine Learning Data Pipeline Apache Kafka 72

phData

NOVEMBER 2, 2023

Managing data pipelines efficiently is paramount for any organization. The Snowflake Data Cloud has introduced a groundbreaking feature that promises to simplify and supercharge this process: Snowflake Dynamic Tables. Flexibility: Dynamic tables allow batch and streaming pipelines to be specified in the same way.

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IBM Journey to AI blog

NOVEMBER 3, 2023

However, not all of it is necessarily actionable and some get stuck in queues or big data batch processing. Additionally, Apache Flink contextualizes your data by detecting patterns, enabling you to understand how things happen alongside each other.

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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

Data Quality Data Lakes Data Warehouse Business Intelligence 52

ODSC - Open Data Science

OCTOBER 18, 2024

The advent of big data, affordable computing power, and advanced machine learning algorithms has fueled explosive growth in data science across industries. However, research shows that up to 85% of data science projects fail to move beyond proofs of concept to full-scale deployment.

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Pickl AI

JUNE 7, 2024

Integration: Airflow integrates seamlessly with other data engineering and Data Science tools like Apache Spark and Pandas. IBM Infosphere DataStage IBM Infosphere DataStage is an enterprise-level ETL tool that enables users to design, develop, and run data pipelines. Read Further: Azure Data Engineer Jobs.

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Pickl AI

OCTOBER 10, 2024

Enhanced Data Quality : These tools ensure data consistency and accuracy, eliminating errors often occurring during manual transformation. Scalability : Whether handling small datasets or processing big data, transformation tools can easily scale to accommodate growing data volumes.

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Mlearning.ai

JULY 11, 2023

This includes important stages such as feature engineering, model development, data pipeline construction, and data deployment. In the data science industry, effective communication and collaboration play a crucial role. Brainstorming sessions are often held to discuss and plan data collection strategies.

Data Science 40

Data Science Exploratory Data Analysis Azure Power BI 40

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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IBM Journey to AI blog

DECEMBER 16, 2022

And because data assets within the catalog have quality scores and social recommendations, Alex has greater trust and confidence in the data she’s using for her decision-making recommendations. This is especially helpful when handling massive amounts of big data. Protected and compliant data.

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Data Quality Data Governance Data Wrangling Data Scientist 52

Pickl AI

SEPTEMBER 5, 2024

Unified Data Services: Azure Synapse Analytics combines big data and data warehousing, offering a unified analytics experience. Azure’s global network of data centres ensures high availability and performance, making it a powerful platform for Data Scientists to leverage for diverse data-driven projects.

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DagsHub

AUGUST 23, 2024

Datafold is a tool focused on data observability and quality. 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.

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Data Quality Data Governance Machine Learning Machine Learning 52

phData

AUGUST 15, 2024

The hype around generative AI has shifted the industry narrative overnight from the big data era of “every company is a data company” to the new belief that “every company is an AI company.” Validating the Data Engineering Strategy There is no one-size-fits-all approach to chunking unstructured data.

AI 52

AI AI Data Engineering Data Engineering 52

Alation

JANUARY 17, 2023

Data ingestion/integration services. Data orchestration tools. These tools are used to manage big data, which is defined as data that is too large or complex to be processed by traditional means. How Did the Modern Data Stack Get Started? What Are the Benefits of a Modern Data Stack? Data scientists.

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Data Warehouse ETL Tableau Cloud Data 52

Snorkel AI

MAY 12, 2023

Compute, big data, large commoditized models—all important stages. But now we’re entering a period where data investments have massive returns from all performance as well as business impact. The reason is that most teams do not have access to a robust data ecosystem for ML development.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

Snorkel AI

MAY 12, 2023

Compute, big data, large commoditized models—all important stages. But now we’re entering a period where data investments have massive returns from all performance as well as business impact. The reason is that most teams do not have access to a robust data ecosystem for ML development.

Machine Learning 52

Machine Learning Machine Learning ML ML 52

The MLOps Blog

AUGUST 11, 2023

This section delves into the common stages in most ML pipelines, regardless of industry or business function. 1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g., Scikit-learn, Feature Tools) 4 Model Training (e.g.,

ML 52

ML ML Machine Learning Machine Learning 52

Flipboard

NOVEMBER 22, 2024

During development, data engineers often spend hours sifting through log files, analyzing execution plans, and making configuration changes to resolve issues. Troubleshooting these production issues requires extensive analysis of logs and metrics, often leading to extended downtimes and delayed insights from critical data pipelines.

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AWS Machine Learning Blog

AUGUST 12, 2024

Data is presented to the personas that need access using a unified interface. For example, it can be used to answer questions such as “If patients have a propensity to have their wearables turned off and there is no clinical telemetry data available, can the likelihood that they are hospitalized still be accurately predicted?”

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