Knowledge Hut

JUNE 26, 2023

From exploratory data analysis (EDA) and data cleansing to data modeling and visualization, the greatest data engineering projects demonstrate the whole data process from start to finish. Data pipeline best practices should be shown in these initiatives.

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Data Engineering Data Engineer Coding Project 98

Knowledge Hut

APRIL 25, 2023

Data engineering is a field that requires a range of technical skills, including database management, data modeling, and programming. Data engineering tools can help automate many of these processes, allowing data engineers to focus on higher-level tasks like extracting insights and building data pipelines.

Data Engineering 52

Data Engineering Data Engineer Engineering Google Cloud 52

Snowflake

APRIL 19, 2023

Welcome to the third blog post in our series highlighting Snowflake’s data ingestion capabilities, covering the latest on Snowpipe Streaming (currently in public preview) and how streaming ingestion can accelerate data engineering on Snowflake. What is Snowpipe Streaming?

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Data Ingestion Kafka Java Data Pipeline 57

Snowflake

MARCH 2, 2023

Snowflake enables organizations to be data-driven by offering an expansive set of features for creating performant, scalable, and reliable data pipelines that feed dashboards, machine learning models, and applications. But before data can be transformed and served or shared, it must be ingested from source systems.

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Kafka Data Ingestion Data Pipeline Cloud Storage 125

phData: Data Engineering

AUGUST 4, 2023

Data storage is a vital aspect of any Snowflake Data Cloud database. Within Snowflake, data can either be stored locally or accessed from other cloud storage systems. What are the Different Storage Layers Available in Snowflake? They are flexible, secure, and provide exceptional performance.

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Cloud Storage Google Cloud Amazon Web Services Data Storage 52

Ascend.io

JUNE 6, 2023

In this blog post, we delve deep into the factors that contribute to how Ascend runs in the cloud, focusing on the primary areas that contribute to costs in this context: storage, compute, networking, and retries. But if reading is not your thing, dive right into the fascinating details of how we master cloud costs in the video below.

Cloud 52

Cloud Data Pipeline Data Ingestion Cloud Storage 52

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

Cloudera

MARCH 31, 2021

One of our customers, Commerzbank, has used the CDP Public Cloud trial to prove that they can combine both Google Cloud and CDP to accelerate their migration to Google Cloud without compromising data security or governance. . Data Preparation (Apache Spark and Apache Hive) .

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Google Cloud Cloud Amazon Web Services Cloud Storage 93

ProjectPro

AUGUST 24, 2021

Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. Google BigQuery receives the structured data from workers.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Knowledge Hut

SEPTEMBER 26, 2023

Born out of the minds behind Apache Spark, an open-source distributed computing framework, Databricks is designed to simplify and accelerate data processing, data engineering, machine learning, and collaborative analytics tasks. This flexibility allows organizations to ingest data from virtually anywhere.

Data Lake 52

Data Lake Database-centric Pipeline-centric Machine Learning 52

Cloudera

OCTOBER 15, 2020

The scenario is the same as it was in the previous blog but the ingest pipeline differs. Spark as the ingest pipeline tool for Search (i.e. In this post, we demonstrated how Cloudera Data Platform components can collaborate with each other, while still being resource isolated and managed separately.

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AWS Data Cloud Cloud Storage 116

Rockset

MARCH 15, 2021

This includes making the data available for query as soon as it is ingested, creating proper indexes on the data so that the query latency is very low, and much more. Before it can be ingested, there’s usually a data pipeline for transforming incoming data. latency, indexing, etc.),

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MongoDB Data Ingestion Analytics Application Kafka 40

Monte Carlo

FEBRUARY 15, 2023

Second, to reduce your time-to-detection you need to be end-to-end across your entire data system which may include warehouses or lakes from other vendors or other components of the modern data stack. Finally, where and how the data pipeline broke isn’t always obvious. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Rockset

NOVEMBER 12, 2020

Elasticsearch is one tool to which reads can be offloaded, and, because both MongoDB and Elasticsearch are NoSQL in nature and offer similar document structure and data types, Elasticsearch can be a popular choice for this purpose. This blog post will examine the various tools that can be used to sync data between MongoDB and Elasticsearch.

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MongoDB NoSQL Data Pipeline Data Storage 40

AltexSoft

JULY 29, 2022

To facilitate data ingestion, there are Apache Flume aggregating log data from multiple servers and Apache Sqoop designed to transport information between Hadoop and relational (SQL) databases. You are welcome to explore them yourself to find the right instruments for your requirements and for data engineering of any complexity.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

Confluent

OCTOBER 16, 2019

After scraping these manually, they’re produced directly into a Kafka topic: kafkacat -b localhost:9092 -t canx_reason_code -P -K: < Data wrangling with stream processing. These transformations make the data usable for both applications and analytics which subscribe to the Kafka topics populated. variation_status" : "LATE".

Kafka 19

Kafka Building Data Coding 19

Monte Carlo

OCTOBER 5, 2023

Aligning with stakeholders: SLAs, SLIs, and SLOs Many organizations adopt an approach to setting data quality standards that will be familiar to stakeholders: SLAs (service-level agreements), SLIs (service-level indicators), and SLOs (service-level objectives). This helps drastically improve time-to-detection.

Government 52

Government Data Governance Data Datasets 52

ProjectPro

MAY 31, 2021

Below is a list of Big Data project ideas and an idea of the approach you could take to develop them; hoping that this could help you learn more about Big Data and even kick-start a career in Big Data. In that case, you must inform the warehouse team to check the stock availability and commit to fulfilling the order.

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Big Data Coding Project Hadoop 52

Towards Data Science

NOVEMBER 4, 2023

I’d like to discuss some popular Data engineering questions: Modern data engineering (DE). Does your DE work well enough to fuel advanced data pipelines and Business intelligence (BI)? Are your data pipelines efficient? and parallel data processing. What is it? ML model training using Airflow.

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Data Engineering Data Engineer Engineering BI 86

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? Recently, there’s been a lot of discussion around whether to go with open source or closed source solutions (the dialogue between Snowflake and Databricks’ marketing teams really brings this to light) when it comes to building your data platform.

Building 52

Building BI Data Lake Data Governance 52

AltexSoft

MARCH 30, 2023

Databricks architecture Databricks provides an ecosystem of tools and services covering the entire analytics process — from data ingestion to training and deploying machine learning models. Besides that, it’s fully compatible with various data ingestion and ETL tools. Let’s see what exactly Databricks has to offer.

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Scala Data Lake BI Google Cloud 64