Confluent

FEBRUARY 6, 2019

Let’s take a look at some of these options: Official standards like Open Neural Network Exchange (ONNX), Portable Format for Analytics (PFA) or Predictive Model Markup Language (PMML): A data scientist builds a model with Python. This approach supports different frameworks, products and cloud services.

Machine Learning 106

Machine Learning Python Kafka Java 106

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. This can be done manually or with a data cleansing tool.

Process 59

Process Data Mining Aggregated Data Portfolio 59

Picnic Engineering

OCTOBER 31, 2023

Datadog’s focus on cloud-native environments closely aligns with our technology stack: deep support and integrations with Kubernetes and main cloud providers. Their contributions to OTEL auto-instrumentation and commitment to support OTEL data within the platform ensure future compatibility. Why did we choose Datadog in the end?

Java 52

Java Aggregated Data Coding Python 52

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. Extracting data from APIs using Python. Uploading the data on HDFS. Utilizing PySpark for reading data.

Hadoop 40

Hadoop Big Data Coding Project 40

Monte Carlo

FEBRUARY 15, 2023

Since the inception of the cloud, there has been a massive push to store any and all data. On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Rockset

OCTOBER 26, 2022

As our database of record, DynamoDB was great at ingesting external data, which we stored within a single table in DynamoDB. One was to create another data pipeline that would aggregate data as it was ingested into DynamoDB. And with the NFL season set to start in less than a month, we were in a bind.

SQL 52

SQL NoSQL Database Aggregated Data 52

Databand.ai

JULY 10, 2023

The Importance of an Integrated Observability Platform As the shift from traditional, single-entity applications to distributed systems and cloud-native technologies continues, managing these structures becomes increasingly complex. This flexibility makes it easier for different teams to focus on the metrics that matter most to them.

Data Pipeline 52

Data Pipeline Algorithm Raw Data Aggregated Data 52

ProjectPro

AUGUST 24, 2021

This is an end-to-end big data project for building a data engineering pipeline involving data extraction, data cleansing, data transformation, exploratory analysis , data visualization, data modeling, and data flow orchestration of event data on the cloud.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

AltexSoft

SEPTEMBER 21, 2023

Accessible via a unified API, these new features enhance search relevance and are available on Elastic Cloud. The Elastic Stacks Elasticsearch is integral within analytics stacks, collaborating seamlessly with other tools developed by Elastic to manage the entire data workflow — from ingestion to visualization.

Engineering 52

Engineering NoSQL Programming Language Java 52

phData: Data Engineering

JANUARY 3, 2022

Analyzing the data, ensuring it adheres to data governance rules and regulations. Understanding the pros and cons of data storage and query options. For example, an enterprise might be using Amazon Web Services (AWS) as a cloud provider, and you want to store and query data from various systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

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. Fig 2: Internal components of Axion Axion’s fact logging client logs facts to the keystone real-time stream processing platform , which outputs data to an Iceberg table.

Metadata 75

Metadata Datasets Machine Learning Designing 75

Cloudera

NOVEMBER 9, 2018

These digital tools will allow them to: Effectively aggregate data from various systems and organizations to support multi-functional analytic applications. Cost-effectively ingest, store and utilize data from all IoT devices. Enable comprehensive data security, compliance, and governance for all of the data collected.

Machine Learning 56

Machine Learning Banking Government Media 56

ProjectPro

JUNE 28, 2016

Kafka is extensively being used across industries for general – purpose messaging system where high availability and real time data integration and analytics are of utmost importance. It provides a scalable and reliable messaging system for cloud based environment for asynchronous messaging distribution.

Kafka 40

Kafka Systems Hadoop BI 40

ProjectPro

JULY 27, 2021

Top 100+ Data Engineer Interview Questions and Answers The following sections consist of the top 100+ data engineer interview questions divided based on big data fundamentals, big data tools/technologies, and big data cloud computing platforms. What are the various types of storage available in Azure?

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40