ProjectPro

JUNE 6, 2025

Confluent enhances Kafka's capabilities with tools such as the Confluent Control Center for monitoring clusters, the Confluent Schema Registry for managing data schemas, and Confluent KSQL for stream processing using SQL -like queries. Find datasets to stream into Kafka, such as weather data or e-commerce transactions.

Kafka 40

Kafka Certification AWS Retail 40

Towards Data Science

MARCH 9, 2023

Although within a big data context, Apache Spark’s MLLib tends to overperform scikit-learn due to its fit for distributed computation, as it is designed to run on Spark. Datasets containing attributes of Airbnb listings in 10 European cities ¹ will be used to create the same Pipeline in scikit-learn and MLLib. Source: The author.

Machine Learning 98

Machine Learning Building Datasets Scala 98

Snowflake

OCTOBER 22, 2024

Rather than scrubbing or redacting sensitive fields — or worse, creating rules to generate “realistic” data from the ground up —you simply point our app at your production schema, train one of the included models, and generate as much synthetic data as you like. It’s basically an “easy button” for synthetic data.

Data Schemas 98

Data Schemas Data Machine Learning Datasets 98

ProjectPro

JUNE 6, 2025

Managing data quality issues in ETL (Extract, Transform, Load) processes is crucial for ensuring the reliability of the transformed data. This involves a systematic approach that begins with data profiling to understand and identify anomalies in the dataset, including outliers and missing values.

Data Engineering 40

Data Engineering Data Engineer Engineering NoSQL 40

ProjectPro

FEBRUARY 8, 2023

You can produce code, discover the data schema, and modify it. Smooth Integration with other AWS tools AWS Glue is relatively simple to integrate with data sources and targets like Amazon Kinesis, Amazon Redshift, Amazon S3, and Amazon MSK. For analyzing huge datasets, they want to employ familiar Python primitive types.

AWS 98

AWS Scala Metadata Data Lake 98

Pinterest Engineering

MAY 2, 2023

We set up a separate dataset for each event type indexed by our system, because we want to have the flexibility to scale these datasets independently. In particular, we wanted our KV store datasets to have the following properties: Allows inserts. We need each dataset to store the last N events for a user.

Lambda Architecture 95

Lambda Architecture Software Engineer Software Engineering Datasets 95

Towards Data Science

NOVEMBER 4, 2023

Indeed, datalakes can store all types of data including unstructured ones and we still need to be able to analyse these datasets. What I like about it is that it makes it really easy to work with various data file formats, i.e. SQL, XML, XLS, CSV and JSON. You can change these # to conform to your data. Datalake example.

Data Engineering 98

Data Engineering Data Engineer Engineering BI 98

ProjectPro

JUNE 6, 2025

MapReduce is a Hadoop framework used for processing large datasets. Another name for it is a programming model that enables us to process big datasets across computer clusters. This program allows for distributed data storage, simplifying complex processing and vast amounts of data. What is MapReduce in Hadoop?

Big Data 40

Big Data Hadoop Relational Database AWS 40

Workfall

SEPTEMBER 18, 2023

Streamline Data Volume for Efficiency: While Snowflake is capable of handling large datasets, it’s essential to be mindful of data volume. Focus on sending relevant, necessary data to Snowflake to prevent overwhelming the integration process. Account for potential changes in data schemas and structures.

Data Pipeline 93

Data Pipeline Raw Data Data Schemas Healthcare 93

ProjectPro

JUNE 6, 2025

Some of the top features of Redshift are: Redshift offers fast query performance on datasets ranging from gigabytes to exabytes in size. Redshift uses data compression, zone maps, and columnar storage to reduce the amount of I/O required for query execution. This format enables the creation and operation of big datasets.

Data Warehouse 40

Data Warehouse Data Mining Recruitment Database 40

ProjectPro

JUNE 6, 2025

Source: LinkedIn Pydantic AI vs Crew AI Pydantic AI focuses on robust data validation and parsing for Python applications. Built on Pydantic, it simplifies handling complex data schemas with automatic type validation and error handling.

Building 40

Building Pipeline-centric Database-centric Data Validation 40

Striim

SEPTEMBER 11, 2024

It supports SQL-based queries for precise data retrieval, batch analytics for processing large datasets, and reporting dashboards for visualizing key metrics and trends. Additionally, it facilitates machine learning applications, allowing for advanced data analysis and predictive insights.

Data Pipeline 98

Data Pipeline Designing Data Lake Data Warehouse 98

Monte Carlo

OCTOBER 11, 2022

Imagine this You’re a data scientist with a swagger working on a predictive model to optimize a fast-growing company’s digital marketing spend. After diligent data exploration, you import a few datasets into your Python notebook. Model design You see the LinkedIn ad click data has.1% Image courtesy of Chad Sanderson.

IT 52

IT Data Warehouse Datasets Data Analysis 52

Monte Carlo

MAY 31, 2024

On the hardware side, the integration of specialized hardware like Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs) into data processing pipelines has revolutionized the speed at which data can be analyzed. This proactive monitoring enables data teams to address problems before they impact business decisions.

Data Science 52

Data Science Data Schemas Machine Learning Data Pipeline 52

Grouparoo

OCTOBER 23, 2021

release of Grouparoo is a huge step forward for data engineers using Grouparoo to reliably sync a variety of types of data to operational tools. Models enable Grouparoo to work with multiple data schemas at once. Now with Models, we can be more sure that all of the Records are in the same dataset and give this option.

AWS 52

AWS Data Schemas Datasets Data Engineering 52

ProjectPro

NOVEMBER 22, 2021

What's the difference between an RDD, a DataFrame, and a DataSet? RDDs contain all datasets and dataframes. If a similar arrangement of data needs to be calculated again, RDDs can be efficiently reserved. It's useful when you need to do low-level transformations, operations, and control on a dataset. count())) df2.show(truncate=False)

Hadoop 52

Hadoop Metadata Java Python 52

Ascend.io

MAY 24, 2023

Let’s take a look at some of the datasets that we receive from hospitals. Biome Analytics receives two types of datasets from hospitals: financial and clinical datasets. The clinical dataset consists of all characteristics, treatments, and outcomes of cardiac disease patients. billion financial records and 8.3

Healthcare 52

Healthcare Data Pipeline Hospitality MySQL 52

DataKitchen

MAY 10, 2024

This blog post explores the challenges and solutions associated with data ingestion monitoring, focusing on the unique capabilities of DataKitchen’s Open Source Data Observability software. This process is critical as it ensures data quality from the onset. Have all the source files/data arrived on time?

Data Ingestion 53

Data Ingestion Transportation Data Schemas High Quality Data 53

Rockset

MARCH 31, 2022

DAY 2 On day 2, as I was learning a data schema I had never seen before, I was able to write the SQL, with some amazing help from Rockset. I extracted a string value containing deeply nested JSON data with multiple arrays, subdocuments, sub arrays, etc.,

MongoDB 52

MongoDB Data Architect Data Schemas SQL 52

Knowledge Hut

APRIL 23, 2024

In the modern data-driven landscape, organizations continuously explore avenues to derive meaningful insights from the immense volume of information available. Two popular approaches that have emerged in recent years are data warehouse and big data. Big data offers several advantages.

Data Warehouse 52

Data Warehouse Big Data Unstructured Data Data Ingestion 52

Booking.com Engineering

DECEMBER 2, 2022

BigQuery also offers native support for nested and repeated data schema[4][5]. We take advantage of this feature in our ad bidding systems, maintaining consistent data views from our Account Specialists’ spreadsheets, to our Data Scientists’ notebooks, to our bidding system’s in-memory data.

Systems 52

Systems Cloud MySQL Relational Database 52

Rockset

SEPTEMBER 20, 2022

On-chain data has to be tied back to relevant off-chain datasets, which can require complex JOIN operations which lead to increased data latency. Image Source There are several companies that enable users to analyze on-chain data, such as Dune Analytics, Nansen, Ocean Protocol, and others.

MongoDB 52

MongoDB PostgreSQL SQL Database 52

AltexSoft

OCTOBER 3, 2022

They allow for representing various types of data and content (data schema, taxonomies, vocabularies, and metadata) and making them understandable for computing systems. So, in terms of a “graph of data”, a dataset is arranged as a network of nodes, edges, and labels rather than tables of rows and columns.

Relational Database 52

Relational Database Banking Pharmaceutical Media 52

Monte Carlo

MAY 31, 2022

“There were a couple of challenges because it’s easy to break this type of pipeline and an analyst would work for quite a while to find the data he’s looking for.” It involves a contract with the client sending the data , schema registry, and pipeline owners responsible for fixing any issues.

BI 52

BI Data Warehouse Unstructured Data Data Schemas 52

Striim

NOVEMBER 8, 2023

Additionally, the decentralized data storage model reduces the time to value for data consumers by eliminating the need to transport data to a central store to power analytics. Data as a product This principle can be summarized as applying product thinking to data.

Architecture 52

Architecture Generalist Government Data 52

Edureka

MAY 27, 2025

It streamlines the handling of various data formats and structures within ETL workflows. In the field of data engineering, DynamicFrame boosts Glue’s capability to manage complex and diverse datasets. AWS Data Engineer Interview Questions for Experienced 17.

AWS 40

AWS Data Engineering Data Engineer Engineering 40

Cloud Academy

JANUARY 27, 2022

For example, it’s good to be familiar with the different data types in the field, including: variables varchar int char prime numbers int numbers Also, named pairs and their storage in SQL structures are important concepts. These fundamentals will give you a solid foundation in data and datasets.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Knowledge Hut

NOVEMBER 3, 2023

Skills Required for MongoDB for Data Science To excel in MongoDB for data science, you need a combination of technical and analytical skills: Database Querying: It is necessary to know how to write sophisticated queries using the query language of MongoDB. Quickly pull (fetch), filter, and reduce data.

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

Knowledge Hut

OCTOBER 4, 2023

Database SQL database Access database Oracle database IBM Netezza MySQL database Sybase database Power Platform Power BI dataset Dataflows 4. It will ingest the data through Power BI and leverage the complete power of machine learning for easy collaboration. Each row will have one or more values that are speared by common.

BI 52

BI Systems Raw Data Certification 52

Monte Carlo

JUNE 26, 2023

Why the Lakehouse Needs Data Observability Data lakes create a ton of unique challenges for data quality. Data lakes often contain larger datasets than what you’d find in a warehouse, including massive amounts of unstructured data that wouldn’t be possible in a warehouse environment.

Data Lake 52

Data Lake Metadata Bytes Google Cloud 52

dbt Developer Hub

FEBRUARY 7, 2022

A customer 360 is a fancy way of saying that you have a holistic dataset that lets understand your customers’ behavior. Make sure you check your dataset to see if this is a valid assumption. Oftentimes, in a CRM’s data schema, there’s a built-in treatment for handling merged entities. What's a customer 360? ​

Data Warehouse 52

Data Warehouse Data SQL Datasets 52

Rockset

DECEMBER 9, 2021

Companies that embraced the modern data stack reaped the rewards, namely the ability to make even smarter decisions with even larger datasets. Now more than ten years old, the modern data stack is ripe for innovation. Real-time insights delivered straight to users, i.e. the modern real-time data stack.

Transportation 52

Transportation BI SQL Data Warehouse 52

Rockset

OCTOBER 6, 2022

Consequently, we needed a data backend with the following characteristics: Scale With ~50 commits per working day (and thus at least 50 pull request updates per day) and each commit running over one million tests, you can imagine the storage/computation required to upload and process all our data.

AWS 52

AWS Data Schemas Accessibility Software Engineer 52

dbt Developer Hub

NOVEMBER 28, 2021

Here are the tools I chose to use: Google Bigquery acts as the main database, holding all the source data, intermediate models, and data marts. This could just as easily have been Snowflake or Redshift, but I chose BigQuery because one of my data sources is already there as a public dataset.

Raw Data 52

Raw Data Metadata Database Datasets 52

ProjectPro

JANUARY 31, 2023

MapReduce is a Hadoop framework used for processing large datasets. Another name for it is a programming model that enables us to process big datasets across computer clusters. This program allows for distributed data storage, simplifying complex processing and vast amounts of data. What is MapReduce in Hadoop?

Big Data 52

Big Data Hadoop Relational Database AWS 52

Knowledge Hut

DECEMBER 28, 2023

Compare and sync servers, data, schema, and other components of the database Transaction Rollback Functionality that mitigates the need for short-term backup. Key Features: Ability to navigate and manage specific database objects like tables and views.

SQL 52

SQL MySQL PostgreSQL Database 52

Knowledge Hut

MARCH 22, 2024

Versatility: The versatile nature of MongoDB enables it to easily deal with a broad spectrum of data types , structured and unstructured, and therefore, it is perfect for modern applications that need flexible data schemas.

MongoDB 52

MongoDB Amazon Web Services Computer Science Education 52