Netflix Tech

APRIL 26, 2022

Since we train our models on several weeks of data, this method is slow for us as we will have to wait for several weeks for the data collection. Each of these models are trained with different datasets and features along with different stratification and objectives. How do we monitor the quality of data?

Metadata 77

Metadata Datasets Machine Learning Designing 77

Rockset

OCTOBER 26, 2022

One was to create another data pipeline that would aggregate data as it was ingested into DynamoDB. And that’s true for small datasets and larger ones. And with the NFL season set to start in less than a month, we were in a bind. A Faster, Friendlier Solution We considered a few alternatives.

SQL 52

SQL NoSQL Database Aggregated Data 52

DareData

JULY 5, 2023

Users: Who are users that will interact with your data and what's their technical proficiency? Data Sources: How different are your data sources? Latency: What is the minimum expected latency between data collection and analytics? And what is their format?

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

ProjectPro

JANUARY 25, 2022

Furthermore, PySpark allows you to interact with Resilient Distributed Datasets (RDDs) in Apache Spark and Python. PySpark is a handy tool for data scientists since it makes the process of converting prototype models into production-ready model workflows much more effortless. RDD uses a key to partition data into smaller chunks.

Big Data 52

Big Data Data Process Process Kafka 52

AltexSoft

AUGUST 10, 2021

If you feel like you strike a match with predictive analytics, keep reading to learn a crucial part: what data the system will require to determine winning attributes. Key data points for predictive lead scoring. Let’s review all data points that can help the engine identify key attributes. Demographic data.

Machine Learning 72

Machine Learning Data Mining Algorithm Datasets 72

ProjectPro

AUGUST 24, 2021

And if you are aspiring to become a data engineer, you must focus on these skills and practice at least one project around each of them to stand out from other candidates. Explore different types of Data Formats: A data engineer works with various dataset formats like.csv,josn,xlx, etc.

Data Engineer 52

Data Engineer Data Engineering Coding Project 52

Confluent

FEBRUARY 6, 2019

While all these solutions help data scientists, data engineers and production engineers to work better together, there are underlying challenges within the hidden debts: Data collection (i.e., Similarly to rapid prototyping with these libraries, you can do interactive queries and data preprocessing with ksql-python.

Machine Learning 107

Machine Learning Python Kafka Java 107

phData: Data Engineering

JANUARY 3, 2022

This likely requires you to aggregate data from your ERP system, your supply chain system, potentially third-party vendors, and data around your internal business structure. Data governance is more focused on data administration, and data engineering is focused on data execution.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Governance 52

AltexSoft

SEPTEMBER 21, 2023

Whether you’re an enterprise striving to manage large datasets or a small business looking to make sense of your data, knowing the strengths and weaknesses of Elasticsearch can be invaluable. Fluentd is a data collector and a lighter-weight alternative to Logstash.

Engineering 52

Engineering NoSQL Programming Language Java 52

ProjectPro

JULY 27, 2021

Data Engineer Interview Questions on Big Data Any organization that relies on data must perform big data engineering to stand out from the crowd. But data collection, storage, and large-scale data processing are only the first steps in the complex process of big data analysis.

Data Engineer 40

Data Engineer Data Engineering Engineering Hadoop 40

ProjectPro

OCTOBER 29, 2021

Real-world databases are often incredibly noisy, brimming with missing and inconsistent data and other issues that are often amplified by their enormous size and heterogeneous sources of origin caused by what seems to be an unending pursuit to amass more data.

Data Mining 52

Data Mining Datasets Machine Learning Metadata 52

DoorDash Engineering

OCTOBER 17, 2023

They subsequently adjust the experiment’s start date so that it does not include metric data collected prior to the bug fix. Using weights in regression allows efficient scaling of the algorithm, even when interacting with large datasets. size() model1 = smf.glm(formula, data=df, freq_weights=df.size.df_aggregated).fit(cov_type="HC1")

Education 87

Education Kafka Algorithm Data Warehouse 87

ProjectPro

APRIL 20, 2017

There are various kinds of hadoop projects that professionals can choose to work on which can be around data collection and aggregation, data processing, data transformation or visualization. The dataset consists of metadata and audio features for 1M contemporary and popular songs.

Hadoop 40

Hadoop Big Data Coding Project 40