Netflix Tech

OCTOBER 23, 2019

Here’s an example using scikit-learn, a Python library, to compute an isotonic regression of a dataset generated with Scala. A polyglot example showing data generation in Scala and data analysis in Python As this example shows, Polynote enables users to fluently move from one language to another within the same notebook.

Scala 94

Scala Machine Learning Python Coding 94

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

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 56

Data Ingestion Transportation High Quality Data Data Schemas 56

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 Datasets Data Warehouse Data Analysis 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

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 Datasets 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 Hadoop 52

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

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.

PostgreSQL 52

PostgreSQL MongoDB SQL Database 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 Datasets 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 Media Computer Science 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 Machine Learning 52

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 Data Preparation 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 Machine Learning 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 Datasets Data SQL 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

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

Monte Carlo

FEBRUARY 26, 2024

Large volumes of data from various sources can be connected and processed, and AI and automated algorithms help automatically detect business rules, as well as assign data quality rules automatically. With Ataccama, AI detects related and duplicate datasets. Did we miss one? Tell us in the comments.

Metadata 40

Metadata Government Data Data Governance 40

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 Accessible 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

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

Netflix Tech

OCTOBER 23, 2019

Here’s an example using scikit-learn, a Python library, to compute an isotonic regression of a dataset generated with Scala. A polyglot example showing data generation in Scala and data analysis in Python As this example shows, Polynote enables users to fluently move from one language to another within the same notebook.

Scala 43

Scala Machine Learning Python Coding 43

phData: Data Engineering

SEPTEMBER 27, 2024

They know where every piece of customer data is, what it means, and who’s been using it. A data catalog is a detailed inventory of all the customer data assets within your organization, including datasets, databases, APIs, and even reports and dashboards. A good data catalog will find them in seconds.

Data 52

Data Raw Data Data Lake Architecture 52

Netflix Tech

OCTOBER 23, 2019

Here’s an example using scikit-learn, a Python library, to compute an isotonic regression of a dataset generated with Scala. A polyglot example showing data generation in Scala and data analysis in Python As this example shows, Polynote enables users to fluently move from one language to another within the same notebook.

Scala 40

Scala Machine Learning Python Coding 40

Monte Carlo

FEBRUARY 6, 2023

Instead of making data easier to find, mental cues and esoteric naming conventions will slow productivity for users, limiting return from a new warehouse and increasing the burden on data engineers to locate datasets or troubleshoot dashboards.

Data Warehouse 52

Data Warehouse AWS Data Data Validation 52

Monte Carlo

APRIL 24, 2023

If streaming data is a priority for your platform, you might also choose to leverage a system like Confluent’s Apache Kafka along with some of the above mentioned technologies. Upstream data evolution breaks pipelines.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

ProjectPro

AUGUST 21, 2015

Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. Schema Schema on Read Schema on Write Best Fit for Applications Data discovery and Massive Storage/Processing of Unstructured data. are all examples of unstructured data.

Hadoop 40

Hadoop Big Data Metadata Java 40

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 Python Datasets Metadata 52

ProjectPro

APRIL 26, 2016

Pig vs Hive Criteria Pig Hive Type of Data Apache Pig is usually used for semi structured data. Used for Structured Data Schema Schema is optional. Hive requires a well-defined Schema. Language It is a procedural data flow language. Follows SQL Dialect and is a declarative language.

Hadoop 40

Hadoop Metadata SQL Database 40

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 79

Data Engineering Data Engineer Engineering BI 79

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 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 Database 52

Monte Carlo

APRIL 20, 2023

A few tips for a safe migration using data lineage: Document current data schema and lineage. This will be important for when you have to cross-reference your old data ecosystem with your new one. Analyze your current schema and lineage. This is similar to the data lineage use case leveraged by Prefect.

Data Warehouse 52

Data Warehouse BI Data Government 52