Knowledge Hut

OCTOBER 27, 2023

In this article, we will be discussing 4 types of d ata Science Projects for resume that can strengthen your skills and enhance your resume: Data Cleaning Exploratory Data Analysis Data Visualization Machine Learning Data Cleaning A   data scientist,   most likely spend nearly 80% of their time cleaning data.

Data Science 96

Data Science Coding Project Datasets 96

Netflix Tech

NOVEMBER 20, 2023

by Jun He , Yingyi Zhang , and Pawan Dixit Incremental processing is an approach to process new or changed data in workflows. The key advantage is that it only incrementally processes data that are newly added or updated to a dataset, instead of re-processing the complete dataset.

Process 86

Process Data Pipeline Datasets SQL 86

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

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 104

Kafka Data Ingestion Datasets Architecture 104

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 Engineering 52

Data Engineering Data Engineer Python Engineering 52

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

AltexSoft

MAY 12, 2021

Particularly, we’ll present our findings on what it takes to prepare a medical image dataset, which models show best results in medical image recognition , and how to enhance the accuracy of predictions. What is to be done to acquire a sufficient dataset? labeling data by medical experts to create a ground-truth dataset.

Medical 72

Medical Healthcare Datasets Machine Learning 72

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

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

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 82

Database Machine Learning Kafka Aggregated Data 82

ProjectPro

FEBRUARY 22, 2022

Data Analysts use SQL to build an inventory management system to help business owners make critical decisions related to inventory planning. Dataset: As an example, you can use this Walmart Dataset on Kaggle. The dataset contains Walmart store sales (Year, Month, Product Category, and Sales) for 2009-2014.

Data Analysis 52

Data Analysis SQL Project Banking 52

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

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

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

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

Striim

OCTOBER 11, 2024

In this architecture, compute resources are distributed across independent clusters, which can grow both in number and size quickly and infinitely while maintaining access to a shared dataset. This setup allows for predictable data processing times as additional resources can be provisioned instantly to accommodate spikes in data volume.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 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

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 77

Metadata Datasets Machine Learning Designing 77

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

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 Engineering 52

Data Engineering Data Engineer Engineering Scala 52

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

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

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

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

U-Next

JUNE 27, 2023

Pivot Tables allow you to retrieve answers to a series of simple questions about your data with minimum effort when given an input table containing tens, scores, or even thousands of rows. It helps aggregate data by any field (column) and do complex computations on it. Let’s consider a typical scenario of sales.

Utilities 52

Utilities Data Analysis Raw Data 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 Pipeline-centric SQL Aggregated Data 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

DareData

JULY 5, 2023

The architecture of a data lake project may contain multiple components, including the Data Lake itself, one or multiple Data Warehouses or one or multiple Data Marts. The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Ascend.io

MARCH 14, 2023

There are different ways you can make data domain products discoverable and sharable. A spreadsheet might be enough for smaller domains, while more complex domains will likely publish their metadata, owners, origins, sample datasets, and schema to a central repository or catalog.

Government 52

Government Architecture Data Lake Data 52

Ascend.io

MARCH 14, 2023

There are different ways you can make data domain products discoverable and sharable. A spreadsheet might be enough for smaller domains, while more complex domains will likely publish their metadata, owners, origins, sample datasets, and schema to a central repository or catalog.

Government 52

Government Architecture Data Lake Data 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 Engineer Software Engineering Data Warehouse 59

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

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 Engineering 52

Data Engineering Data Engineer Coding Project 52

ProjectPro

NOVEMBER 15, 2021

Multi-node, multi-GPU deployments are also supported by RAPIDS, allowing for substantially faster processing and training on much bigger datasets. TDengine Source: www.taosdata.com TDengine is an open-source big data platform tailored for IoT , linked automobiles, and industrial IoT. Trino Source: trino.io

Big Data 52

Big Data Project Metadata Programming Language 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

DECEMBER 7, 2021

A pipeline may include filtering, normalizing, and data consolidation to provide desired data. It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications. Step 3- Ensuring the accuracy and reliability of data within Lakehouse.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52