Lyft Engineering

APRIL 21, 2023

However, consuming this raw data presents several pain points: The number of requests varies across models; some receive a large number of requests, while others receive only a few. For some models, aggregating data with simple queries is easy, while for others the data is too large to process on a single machine.

Systems 107

Systems Building Machine Learning Datasets 107

Netflix Tech

JULY 12, 2019

Gatekeeper accomplishes its prescribed task by aggregating data from multiple upstream systems, applying some business logic, then producing an output detailing the status of each video in each country. High-Density : encoding, bit-packing, and deduplication techniques are employed to optimize the memory footprint of the dataset.

Datasets 73

Datasets Kafka Architecture Computer Science 73

Lyft Engineering

NOVEMBER 29, 2023

At Lyft, we used rollup as a data preprocessing technique which aggregates and reduces the granularity of data prior to being stored in segments. Pre-aggregating data at ingestion time helped optimize our query performance and reduce our storage costs. An example of how we use Druid rollup at Lyft.

Kafka 106

Kafka Data Ingestion Architecture Datasets 106

Cloudera

FEBRUARY 16, 2021

Integrated across the Enterprise Data Lifecycle . Cloudera Operational Database (COD) plays the crucial role of a data store in the enterprise data lifecycle. You can use COD with: Cloudera DataFlow to ingest and aggregate data from various sources. Cloudera Data Warehouse to perform ETL operations.

Database 83

Database Machine Learning Kafka Data Lake 83

Knowledge Hut

APRIL 19, 2023

The process of merging and summarizing data from various sources in order to generate insightful conclusions is known as data aggregation. The purpose of data aggregation is to make it easier to analyze and interpret large amounts of data. Let's look at the use case of data aggregation below.

Process 59

Process Data Mining Aggregated Data Portfolio 59

Cloudyard

APRIL 24, 2024

Data Privacy: Protecting the confidentiality of individual customer details and adhering to any relevant data privacy regulations. To address this concern, Cloudyard implements an aggregation policy on the shared transaction dataset.

Aggregated Data 52

Aggregated Data Datasets Accessibility Accessible 52

Cloudera

NOVEMBER 13, 2020

Apache Impala is synonymous with high-performance processing of extremely large datasets, but what if our data isn’t huge? It turns out that Apache Impala scales down with data just as well as it scales up. So clearly Impala is used extensively with datasets both small and large. The entire collection is available here.

Metadata 143

Metadata Coding SQL Database 143

Towards Data Science

FEBRUARY 2, 2024

And while there’s certainly value in its simplicity, it doesn’t capture the full value of the data team. If the data systems went down, these activities would still happen, but they would be considerably more painful. But in this case, we aren’t as interested in the aggregate data downtime or the efficiency of the team (yet).

Data 98

Data Aggregated Data Machine Learning Data Mining 98

Cloudyard

JUNE 11, 2024

Imagine you’re tasked with managing a critical data pipeline in Snowflake that processes and transforms large datasets. This pipeline consists of several sequential tasks: Task A: Loads raw data into a staging table. Task B: Transforms the data in the staging table.

Raw Data 52

Raw Data Aggregated Data Data Pipeline Data Workflow 52

Knowledge Hut

APRIL 23, 2024

View A broader view of data Narrower view of data Data Data is gleaned from diverse sources. Results Broader and exploratory results Targeted results Big Data vs Data Mining Here is a more detailed illustration of the difference between big data and data mining:- 1.

Data Mining 52

Data Mining Big Data Database-centric Unstructured Data 52

Confluent

FEBRUARY 6, 2019

Similarly to rapid prototyping with these libraries, you can do interactive queries and data preprocessing with ksql-python. Check out the KSQL quick start and KSQL recipes to understand how to write a KSQL query to easily filter, transform, enrich or aggregate data. The use case is fraud detection for credit card payments.

Machine Learning 107

Machine Learning Python Kafka Java 107

Netflix Tech

APRIL 26, 2022

Each of these models are trained with different datasets and features along with different stratification and objectives. Given that Axion is used as the defacto Fact store for assembling the training dataset for all these models, it is important for Axion to log and store enough facts that would be sufficient for all these models.

Metadata 78

Metadata Datasets Machine Learning Designing 78

ProjectPro

FEBRUARY 8, 2023

Scale Existing Python Code with Ray Python is popular among data scientists and developers because it is user-friendly and offers extensive built-in data processing libraries. For analyzing huge datasets, they want to employ familiar Python primitive types. Then Redshift can be used as a data warehousing tool for this.

AWS 98

AWS Scala Metadata Data Lake 98

Rockset

APRIL 18, 2023

Rockset offers a number of benefits along with vector search support to create relevant experiences: Real-Time Data: Ingest and index incoming data in real-time with support for updates. Feature Generation: Transform and aggregate data during the ingest process to generate complex features and reduce data storage volumes.

Unstructured Data 105

Unstructured Data Metadata Machine Learning SQL 105

LinkedIn Engineering

DECEMBER 20, 2023

At the core of inference at scale lies the fusion of ML with a wealth of data. ML models, meticulously trained on diverse and high-quality datasets, are the linchpin of our Abuse detection efforts. By scrutinizing patterns with abuse data, we pinpoint emerging trends, allowing us to fine-tune our models and systems in real-time.

Building 91

Building Algorithm Kafka Machine Learning 91

Knowledge Hut

JANUARY 25, 2024

What is Data Cleaning? Data cleaning, also known as data cleansing, is the essential process of identifying and rectifying errors, inaccuracies, inconsistencies, and imperfections in a dataset. It involves removing or correcting incorrect, corrupted, improperly formatted, duplicate, or incomplete data.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

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.

Data Pipeline 93

Data Pipeline Raw Data Data Schemas Healthcare 93

Airbnb Tech

APRIL 30, 2021

While we have previously shared how we ingest data into our data warehouse and how to enable users to conduct their own analyses with contextual data , we have not yet discussed the middle layer: how to properly model and transform data into accurate, analysis-ready datasets. Our work hardly stopped there, however.

Data Warehouse 52

Data Warehouse Finance Metadata Aggregated Data 52

Rockset

JUNE 3, 2022

Our campaign decision engine already relies on ML-trained algorithms, but they were built using a limited dataset due to the high cost of integrating disparate datasets. That’s not an issue with Rockset, which naturally aggregates data as it is ingested.

Hospitality 52

Hospitality Aggregated Data Datasets Retail 52

Monte Carlo

FEBRUARY 15, 2023

Google BigQuery BigQuery is famous for giving users access to public health datasets and geospatial data. It has connectors to retrieve data from Google Analytics and all other Google platforms. It also natively integrates with Apache Spark. It has its own notebooks, dataflow integrations, and spark job definitions.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Ascend.io

SEPTEMBER 14, 2023

High Performance Python is inherently efficient and robust, enabling data engineers to handle large datasets with ease: Speed & Reliability: At its core, Python is designed to handle large datasets swiftly , making it ideal for data-intensive tasks.

Data Engineer 52

Data Engineer Data Engineering Python Engineering 52

Monte Carlo

JANUARY 24, 2024

And while there’s certainly value in its simplicity, it doesn’t capture the full value of the data team. If the data systems went down, these activities would still happen, but they would be considerably more painful. But in this case, we aren’t as interested in the aggregate data downtime or the efficiency of the team (yet).

Data 69

Data Aggregated Data Machine Learning Data Mining 69

Knowledge Hut

OCTOBER 13, 2023

As per Microsoft, “A Power BI report is a multi-perspective view of a dataset, with visuals representing different findings and insights from that dataset. ” Reports and dashboards are the two vital components of the Power BI platform, which are used to analyze and visualize data. Use descriptive names.

BI 52

BI Business Analyst Datasets Raw Data 52

Knowledge Hut

SEPTEMBER 27, 2023

SPICE, an in-memory computation engine, is used to ensure rapid data analysis. SPICE is capable of handling large datasets, allowing for real-time analytics and interactive dashboards Power BI's DAX (Data Analysis Expressions) language prioritizes performance.

BI 52

BI AWS Database-centric Data Lake 52

ProjectPro

JANUARY 5, 2017

She also says that they need more data related to fertility. They are very much anxious to know what the app might do and what they may be able to learn from the aggregated data. Source: [link] Journey Analytics: A Killer App for Big Data? Glow has developed an app which allows to track the menstrual cycles.

Hadoop 52

Hadoop Manufacturing Pharmaceutical BI 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

JANUARY 6, 2022

However, it might not be ideal for time series data because it requires importing all helper classes for the year, month, week, and day formatters. It's also inconvenient when dealing with several datasets, but converting a dataset into a long format and plotting it is simple. total size of data’).

Python 52

Python Datasets Programming Language Data Science 52

ProjectPro

JANUARY 24, 2023

The next step would be to transform it and load it into a data warehouse for further analysis. Azure Data Factory Dataflows can come in handy for this big data project for - Joining and aggregating data from diverse sources like social media, sales, and customer behavior data to build a single 360 degree of the customer.

Retail 52

Retail Big Data Data Pipeline Media 52

AltexSoft

JANUARY 26, 2022

For more detailed information on data science team roles, check our video. An analytics engineer is a modern data team member that is responsible for modeling data to provide clean, accurate datasets so that different users within the company can work with them. Data modeling. What is an analytics engineer?

Engineering 59

Engineering Software Engineering Software Engineer Data Warehouse 59

Ascend.io

NOVEMBER 21, 2023

This process can encompass a wide range of activities, each aiming to enhance the data’s usability and relevance. For example: Aggregating Data: This includes summing up numerical values and applying mathematical functions to create summarized insights from the raw data.

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

ProjectPro

JANUARY 25, 2022

Furthermore, PySpark allows you to interact with Resilient Distributed Datasets (RDDs) in Apache Spark and Python. Because of its interoperability, it is the best framework for processing large datasets. Easy Processing- PySpark enables us to process data rapidly, around 100 times quicker in memory and ten times faster on storage.

Big Data 52

Big Data Data Process Process Kafka 52

Knowledge Hut

JANUARY 19, 2024

New Analytics Strategy vs. Existing Analytics Strategy Business Intelligence is concerned with aggregated data collected from various sources (like databases) and analyzed for insights about a business' performance. BAs help companies make better decisions by identifying patterns and trends in existing data sets.

Business Intelligence 52

Business Intelligence BI Business Analyst Aggregated Data 52

Rockset

NOVEMBER 1, 2022

Here’s an example: SELECT NGRAMS(my_text_string, 1, 3) AS my_text_array, * FROM _input Aggregation It is common to pre-aggregate data before it arrives into Elasticsearch for use cases involving metrics. We often see ingest queries aggregate data by time.

Database-centric 52

Database-centric SQL Pipeline-centric Aggregated Data 52

Knowledge Hut

NOVEMBER 17, 2023

Understanding SQL You must be able to write and optimize SQL queries because you will be dealing with enormous datasets as an Azure Data Engineer. To be an Azure Data Engineer, you must have a working knowledge of SQL (Structured Query Language), which is used to extract and manipulate data from relational databases.

Data Engineer 52

Data Engineer Data Engineering Engineering Scala 52

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

Striim

JULY 10, 2024

Data transformation includes normalizing data, encoding categorical variables, and aggregating data at the appropriate granularity. This step is pivotal in ensuring data consistency and relevance, essential for the accuracy of subsequent predictive models. The next phase is model development.

Management 52

Management Transportation Machine Learning High Quality Data 52

DoorDash Engineering

APRIL 12, 2023

The lack of tracking for the quality and freshness of upstream datasets used in the metric definitions posed a risk of basing important business decisions on outdated or low-quality data. After considering the aforementioned factors and studying other existing metric frameworks, we decided to adopt standard BI data models.

SQL 83

SQL Metadata Raw Data Government 83