Monte Carlo

FEBRUARY 15, 2023

Data Security Data Warehouses achieve security in multiple ways. For example, some data warehouses: Can only be accessed using a private cloud. Can only be accessed using a specific machine or location. Can only be accessed during a certain time of the day Can only be accessed using multi-factor authentication.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Ascend.io

SEPTEMBER 14, 2023

We’ll explore its advantages, delve into its applications, and highlight why Python is increasingly becoming the first choice for data engineers worldwide. Why Python for Data Engineering? As the field of data engineering evolves, the need for a versatile, performant, and easily accessible language becomes paramount.

Data Engineering 52

Data Engineering Data Engineer Python Engineering 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

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 Data Lake Kafka 83

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. The limitation here is we can attach the trigger to only 2 crawlers.

AWS 98

AWS Scala Metadata Data Lake 98

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. Medical image databases: abundant but hard to access. What is to be done to acquire a sufficient dataset?

Medical 72

Medical Healthcare Datasets Machine Learning 72

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

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

DareData

JULY 5, 2023

The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format. Typically, it is advisable to retain the data in its original, unaltered format when transferring it from any source to the data lake layer.

Data Lake 52

Data Lake Building Raw Data ETL Tools 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

Rockset

APRIL 18, 2023

Vector search has seen an explosion in popularity due to improvements in accuracy and broadened accessibility to the models used to generate embeddings. 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.

Unstructured Data 105

Unstructured Data Metadata Machine Learning SQL 105

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. If we can access the historical facts, we can regenerate the features using updated feature encoders. fact logging client, ETL, query client, and data quality infrastructure.

Metadata 75

Metadata Datasets Machine Learning AWS 75

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. This includes taking measures such as issuing warnings, restricting access, or suspending accounts as necessary.

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 Aggregated Data 73

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

Ascend.io

MARCH 14, 2023

Data represents our present and our future, and therein lies a significant problem: the more data you’re dealing with, the more challenging it will be to scale your company in a sustainable and standardized way. A monolithic data platform stores all this data in one place. So, what’s the solution?

Government 52

Government Architecture Data Lake Data 52

Ascend.io

MARCH 14, 2023

Data represents our present and our future, and therein lies a significant problem: the more data you’re dealing with, the more challenging it will be to scale your company in a sustainable and standardized way. A monolithic data platform stores all this data in one place. So, what’s the solution?

Government 52

Government Architecture Data Lake Data 52

ProjectPro

OCTOBER 29, 2021

Data preprocessing is a step that involves transforming raw data so that issues owing to the incompleteness, inconsistency, and/or lack of appropriate representation of trends are resolved so as to arrive at a dataset that is in an understandable format.

Data Mining 52

Data Mining Datasets Machine Learning Metadata 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. After setting up Airbyte, you would see the UI as something like this.

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

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

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

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 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

DoorDash Engineering

APRIL 12, 2023

Additionally, having a centralized repository of metrics facilitates easy access to metrics, empowering all members of an organization to analyze experiment results. Data scientists are the primary metric creators and are already familiar with SQL, so it made sense to use SQL as the language to define metrics instead of building our own DSL.

SQL 82

SQL Metadata Raw Data Government 82

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

ProjectPro

NOVEMBER 15, 2021

When any particular project is open-sourced, it makes the source code accessible to anyone. The adaptability and technical superiority of such open-source big data projects make them stand out for community use. DataFrames are used by Spark SQL to accommodate structured and semi-structured data.

Big Data 52

Big Data Project Metadata Programming Language 52

AltexSoft

MARCH 14, 2023

That’s why some MDS tools are commercial distributions designed to be low-code or even no-code, making them accessible to data practitioners with minimal technical expertise. This means that companies don’t necessarily need a large data engineering team. Data democratization.

IT 59

IT Data Warehouse Data Governance Data Lake 59

AltexSoft

AUGUST 10, 2021

This process is called lead scoring and with access to data analytics, it allows you to predict how much each lead matters with utmost accuracy. 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. Demographic data.

Machine Learning 72

Machine Learning Data Mining Datasets Algorithm 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. It can also be made accessible as an API and distributed to stakeholders.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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

phData: Data Engineering

JANUARY 3, 2022

The big data analytics market is set to reach $103 billion by 2023 , with poor data quality costing the US economy up to $3.1 Fortune 1000 companies can gain more than $65 million additional net income, only by increasing their data accessibility by 10%. How do I audit and provision access? trillion yearly.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

ProjectPro

APRIL 20, 2017

Having multiple hadoop projects on your resume will help employers substantiate that you can learn any new big data skills and apply them to real life challenging problems instead of just listing a pile of hadoop certifications. Creating query to populate and filter the data. Analyse log files in HIVE.

Hadoop 40

Hadoop Big Data Coding Project 40

Rockset

APRIL 15, 2022

Queries must be double-checked that they are pulling data from the right locations or run the risk of data errors. Just imagine the overhead and confusion for an application developer when accessing the latest version of a record. Both CDC and data enrichment boosted the accuracy and reach of their analytics.

Analytics Application 52

Analytics Application Data Warehouse Raw Data Kafka 52

DoorDash Engineering

OCTOBER 17, 2023

Using weights in regression allows efficient scaling of the algorithm, even when interacting with large datasets. With this approach, we don’t just perform the regression computation more efficiently, we also minimize any network transfer costs and latencies and can perform much of the aggregation to get the inputs on the data warehouse.

Education 87

Education Kafka Algorithm Data Warehouse 87

Confluent

FEBRUARY 6, 2019

The data scientist “just” deploys its trained model, and production engineers can access it. 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.,

Machine Learning 106

Machine Learning Python Kafka Java 106

Rockset

FEBRUARY 25, 2021

Instead, this data is often semi-structured in JSON or arrays. Often this lack of structure forces developers to spend a lot of their time engineering ETL and data pipelines so that analysts can access the complex datasets. With the many data sources in today’s modern architecture, this can be difficult.

SQL 40

SQL Data Pipeline Kafka Database 40