Hepta Analytics

FEBRUARY 14, 2022

DE Zoomcamp 2.2.1 – Introduction to Workflow Orchestration Following last weeks blog , we move to data ingestion. We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design.

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phData: Data Engineering

NOVEMBER 8, 2024

This is particularly beneficial in complex analytical queries, where processing smaller, targeted segments of data results in quicker and more efficient query execution. Additionally, the optimized query execution and data pruning features reduce the compute cost associated with querying large datasets.

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Architecture Systems Data Lake Google Cloud 52

phData: Data Engineering

FEBRUARY 21, 2025

The Cloud represents an iteration beyond the on-prem data warehouse, where computing resources are delivered over the Internet and are managed by a third-party provider. Examples include: Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Data integrations and pipelines can also impact latency.

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Cloud Data Warehouse Amazon Web Services Data Ingestion 52

Confluent

FEBRUARY 6, 2019

It allows real-time data ingestion, processing, model deployment and monitoring in a reliable and scalable way. This blog post focuses on how the Kafka ecosystem can help solve the impedance mismatch between data scientists, data engineers and production engineers.

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Machine Learning Python Kafka Java 107

Data Engineering Podcast

NOVEMBER 13, 2022

report having current investments in automation, 85% of data teams plan on investing in automation in the next 12 months. The Ascend Data Automation Cloud provides a unified platform for data ingestion, transformation, orchestration, and observability. In fact, while only 3.5% That’s where our friends at Ascend.io

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MongoDB MySQL Google Cloud Scala 100

Towards Data Science

MARCH 5, 2024

In that case, queries are still processed using the BigQuery compute infrastructure but read data from GCS instead. Such external tables come with some disadvantages but in some cases it can be more cost efficient to have the data stored in GCS. Load data For data ingestion Google Cloud Storage is a pragmatic way to solve the task.

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Bytes Google Cloud Cloud Storage Utilities 97

Confluent

MAY 22, 2019

Since MQTT is designed for low-power and coin-cell-operated devices, it cannot handle the ingestion of massive datasets. On the other hand, Apache Kafka may deal with high-velocity data ingestion but not M2M. We use the Google Cloud API to automate the deployment of a ScyllaDB cluster. Google Cloud SDK.

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Kafka Google Cloud NoSQL Entertainment 98

Confluent

OCTOBER 10, 2019

MQTT Proxy for data ingestion without an MQTT broker. In some scenarios, the main challenge and requirement is to ingest data into Kafka for further processing and analytics in other backend systems.

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Kafka Google Cloud Architecture Machine Learning 20

Ascend.io

DECEMBER 21, 2022

21, 2022 – Ascend.io , The Data Automation Cloud, today announced they have partnered with Snowflake , the Data Cloud company, to launch Free Ingest , a new feature that will reduce an enterprise’s data ingest cost and deliver data products up to 7x faster by ingesting data from all sources into the Snowflake Data Cloud quickly and easily.

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Cloudera

DECEMBER 15, 2022

Since we announced the general availability of Apache Iceberg in Cloudera Data Platform (CDP), Cloudera customers, such as Teranet , have built open lakehouses to future-proof their data platforms for all their analytical workloads. Cloudera partners are also benefiting from Apache Iceberg in CDP.

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Monte Carlo

SEPTEMBER 25, 2024

A cloud-native data lake on Google Cloud. Cloud-Native Data Lake for Scalability and Security : By centralizing over 400 terabytes of business data on Google Cloud, Accenture enhanced data visibility and security—vital for handling sensitive client information.

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Data Lake Food Google Cloud AWS 52

Knowledge Hut

JUNE 26, 2023

Finnhub API with Kafka for Real-Time Financial Market Data Pipeline Project Overview: The goal of this project is to construct a streaming data pipeline by making use of the real-time financial market data API provided by Finnhub.

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Data Engineer Data Engineering Coding Project 98

Rockset

MARCH 21, 2019

This makes turning any type of data—from JSON, XML, Parquet, and CSV to even Excel files—into SQL tables a trivial pursuit. We automatically build multiple general-purpose indexes on all data ingested into Rockset, so that we can eliminate the need for database administration and query tuning for a wide spectrum of applications.

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SQL Data Management Management Engineering 52

ProjectPro

FEBRUARY 21, 2023

Vendor-Specific Data Engineering Certifications The vendor-specific data engineer certifications help you enhance your knowledge and skills relevant to specific vendors, such as Azure, Google Cloud Platform, AWS, and other cloud service vendors. The rest of the exam details are the same as the DP-900 exam.

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Certification Data Engineer Data Engineering Engineering 52

Knowledge Hut

APRIL 25, 2023

Here, we'll take a look at the top data engineer tools in 2023 that are essential for data professionals to succeed in their roles. These tools include both open-source and commercial options, as well as offerings from major cloud providers like AWS, Azure, and Google Cloud. What are Data Engineering Tools?

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Data Engineer Data Engineering Engineering Google Cloud 52

Hevo

AUGUST 16, 2024

This blog explores the world of open source data orchestration tools, highlighting their importance in managing and automating complex data workflows. From Apache Airflow to Google Cloud Composer, we’ll walk you through ten powerful tools to streamline your data processes, enhance efficiency, and scale your growing needs.

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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 Machine Learning BI 64

Monte Carlo

APRIL 24, 2023

We continuously hear data professionals describe the advantage of the Snowflake platform as “it just works.” Snowpipe and other features makes Snowflake’s inclusion in this top data lake vendors list a no-brainer. This is a lot of work and for most companies, it takes them several months to set up a data lake.

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Data Lake Google Cloud Data Warehouse AWS 52

Booking.com Engineering

DECEMBER 2, 2022

In this article, we want to illustrate our extensive use of the public cloud, specifically Google Cloud Platform (GCP). From data ingestion, data science, to our ad bidding[2], GCP is an accelerant in our development cycle, sometimes reducing time-to-market from months to weeks. What are PPC’s Challenges?

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ProjectPro

AUGUST 24, 2021

Data Engineering Project for Beginners If you are a newbie in data engineering and are interested in exploring real-world data engineering projects, check out the list of data engineering project examples below. This big data project discusses IoT architecture with a sample use case.

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Data Engineer Data Engineering Coding Project 52

RandomTrees

SEPTEMBER 27, 2024

Microsoft Fabric architecture: The core components of the Microsoft Fabric Seven workloads are part of the Microsoft Fabric architecture, and they operate on top of One Lake, the storage layer that eventually pulls data from Google Cloud Platform as well as Microsoft platforms and Amazon S3.

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Database-centric Pipeline-centric IT BI 52

phData: Data Engineering

AUGUST 4, 2023

The external stage area includes Microsoft Azure Blob storage, Amazon AWS S3, and Google Cloud Storage. Cloud Storage Snowflake leverages the cloud’s native object storage services (e.g. Object storage provides virtually infinite storage capacity and ensures the durability and availability of data.

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Cloud Storage Google Cloud Amazon Web Services Data Storage 52

AltexSoft

MAY 12, 2023

Tools and platforms for unstructured data management Unstructured data collection Unstructured data collection presents unique challenges due to the information’s sheer volume, variety, and complexity. The process requires extracting data from diverse sources, typically via APIs. Hadoop, Apache Spark).

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Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? Databricks – Databricks, the Apache Spark-as-a-service platform, has pioneered the data lakehouse, giving users the options to leverage both structured and unstructured data and offers the low-cost storage features of a data lake.

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Edureka

FEBRUARY 9, 2023

As the demand for data engineers grows, having a well-written resume that stands out from the crowd is critical. Azure data engineers are essential in the design, implementation, and upkeep of cloud-based data solutions. It is also crucial to have experience with data ingestion and transformation.

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Data Engineer Data Engineering Engineering Amazon Web Services 52

dbt Developer Hub

JANUARY 14, 2023

The builder: I’m a data freelancer who deploys end to end solutions, so when I have a data problem, I cannot just let it go. The tools: I want to be able to run my project on Google Cloud Functions due to the generous free tier. Finally, I will use dbt Core for transformation.

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Data Ingestion Python Google Cloud Cloud 52

Rockset

JANUARY 9, 2023

Technical Challenges Our original data infrastructure was built around an on-premises MongoDB database that ingested and stored all user transaction data. However, the one big hole I couldn’t fill was the lack of real-time data ingestion. First is its speed at data ingestion.

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MySQL MongoDB Recruitment Data Ingestion 52

Monte Carlo

JUNE 2, 2024

Cloud Platforms: Understanding cloud services from providers like AWS (mentioned in 80% of job postings), Azure (66%), and Google Cloud (56%) is crucial. Data Pipeline Tools: Familiarity with tools such as Apache Kafka (mentioned in 71% of job postings) and Apache Spark (66%) is vital.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

Monte Carlo

JUNE 2, 2024

Cloud Platforms: Understanding cloud services from providers like AWS (mentioned in 80% of job postings), Azure (66%), and Google Cloud (56%) is crucial. Data Pipeline Tools: Familiarity with tools such as Apache Kafka (mentioned in 71% of job postings) and Apache Spark (66%) is vital.

Engineering 52

Engineering Amazon Web Services Data Science AWS 52

ProjectPro

JANUARY 19, 2022

Cloud Services Providers Platforms As companies are gradually becoming more inclined towards investing in cloud computing for storing their data instead of bulky hardware systems, engineers who can work on cloud computing tools are in demand. It nicely supports Hybrid Cloud Space.

Data Engineer 52

Data Engineer Data Engineering Engineering Amazon Web Services 52

Confluent

JULY 3, 2019

Twitter represents the default source for most event streaming examples, and it’s particularly useful in our case because it contains high-volume event streaming data with easily identifiable keywords that can be used to filter for relevant topics. Ingesting Twitter data. Sentiment; import com.google.cloud.language.v1.AnalyzeSentimentResponse;

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Data Analysis Kafka Datasets Java 21

Confluent

OCTOBER 16, 2019

We want to resolve the location code ( loc_stanox ), and we can do so using the location reference data from the CIF data ingested into a separate Kafka topic and modelled as a KSQL table: SELECT EVENT_TYPE, ACTUAL_TIMESTAMP, LOC_STANOX, S.TPS_DESCRIPTION AS LOCATION_DESCRIPTION FROM TRAIN_MOVEMENTS_00 TM.

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Kafka Building Data Coding 19

Striim

JUNE 4, 2024

Contrary to traditional methods, such as batch processing where data is collected, stored, and analyzed at a later time, with real-time processing there’s no delay even for high-velocity data sets. This data must be ingested with minimal latency to ensure it is available for immediate processing.

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Machine Learning Algorithm Healthcare Utilities 52

Knowledge Hut

MARCH 19, 2024

market share, while all of its rivals combined, Microsoft Azure (29.4%), Google Cloud (3.0%), and IBM (2.6%), do not even reach that percentage. That shows how much AWS has to offer, and you must know about it if you’re a cloud computing enthusiast. Certainly, AWS is the market leader since it has a 41.5%

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Snowflake

AUGUST 16, 2023

The Snowflake Native App Framework remains available in private preview on Google Cloud Platform and Azure. Data comes in a continuous manner, and often a separate architecture is required to handle streaming data. What remains challenging is how streaming data is brought together with batch data.

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Transportation Scala Kafka Data Lake 52

Striim

OCTOBER 11, 2024

As you’ll see by taking a look at this data pipeline example, the complexity and design of a pipeline varies depending on intended use. For instance, Macy’s streams change data from on-premises databases to Google Cloud. Another excellent data pipeline example is American Airlines’ work with Striim.

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Data Pipeline MongoDB Unstructured Data Data Lake 52

Striim

APRIL 1, 2024

Striim Cloud for Application Integration is a fully managed, unified data integration and streaming service designed to transport data across clouds, applications, and databases to BigQuery and other Google Cloud targets.

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Hospitality Transportation Google Cloud Cloud 52