Setting up Data Lake on GCP using Cloud Storage and BigQuery
Analytics Vidhya
FEBRUARY 25, 2023
The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.
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Analytics Vidhya
FEBRUARY 25, 2023
The need for a data lake arises from the growing volume, variety, and velocity of data companies need to manage and analyze.
Start Data Engineering
AUGUST 17, 2021
Batch Data Pipelines 1.1 Process => Data Warehouse 1.2 Process => Cloud Storage => Data Warehouse 2. Near Real-Time Data pipelines 2.1 Data Stream => Consumer => Data Warehouse 2.2
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Cloudera
SEPTEMBER 29, 2020
Performance is one of the key, if not the most important deciding criterion, in choosing a Cloud Data Warehouse service. In today’s fast changing world, enterprises have to make data driven decisions quickly and for that they rely heavily on their data warehouse service. . benchmark.
How to Optimize the Developer Experience for Monumental Impact
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Understanding User Needs and Satisfying Them
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You Need to Know
Leading the Development of Profitable and Sustainable Products
Monte Carlo
FEBRUARY 6, 2023
So, you’re planning a cloud data warehouse migration. But be warned, a warehouse migration isn’t for the faint of heart. As you probably already know if you’re reading this, a data warehouse migration is the process of moving data from one warehouse to another. A worthy quest to be sure.
Cloudera
FEBRUARY 9, 2021
Today’s customers have a growing need for a faster end to end data ingestion to meet the expected speed of insights and overall business demand. This ‘need for speed’ drives a rethink on building a more modern data warehouse solution, one that balances speed with platform cost management, performance, and reliability.
Christophe Blefari
SEPTEMBER 28, 2023
That's why big data technologies got swooshed by the modern data stack when it arrived on the market—excepting Spark. We jumped from HDFS to Cloud Storage (S3, GCS) for storage and from Hadoop, Spark to Cloud warehouses (Redshift, BigQuery, Snowflake) for processing.
Cloudera
DECEMBER 10, 2020
Why worry about costs with cloud-native data warehousing? Have you been burned by the unexpected costs of a cloud data warehouse? If so, you know about the failed economics of some cloud-native solutions on the market today. These costs impede the adoption of cloud-native data warehouses.
Monte Carlo
APRIL 24, 2023
By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases. See our post: Data Lakes vs. Data Warehouses.
Ascend.io
FEBRUARY 23, 2024
If your core data systems are still running in a private data center or pushed to VMs in the cloud, you have some work to do. To take advantage of cloud-native services, some of your data must be replicated, copied, or otherwise made available to native cloud storage and databases.
phData: Data Engineering
APRIL 4, 2023
Customers who don’t necessarily want to put their data directly into a data warehouse like the Snowflake Data Cloud can now use Fivetran to build a performant, governed, managed dataset on top of S3 which can still be efficiently queried and manipulated from within their query engine of choice.
ThoughtSpot
MAY 31, 2023
All communication across tenant-specific compute instances, the common services, and external interaction with your cloud data warehouse are secured over the transport layer security (TLS) channel. Search and model assist hints are stored in the tenant specific cloud storage bucket.
Ascend.io
AUGUST 31, 2023
Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape. Read More: What is ETL?
RandomTrees
SEPTEMBER 6, 2020
Snowflake Overview A data warehouse is a critical part of any business organization. Lot of cloud-based data warehouses are available in the market today, out of which let us focus on Snowflake. Snowflake is an analytical data warehouse that is provided as Software-as-a-Service (SaaS).
Ascend.io
JUNE 22, 2023
Next, build out connections to your sources and pipelines that deliver data to MotherDuck. In the example below, we connected to a MySQL database, Snowflake Data Warehouse and Google Cloud Storage (blob storage). You have tons of other options available in Ascend’s connection catalog!
Cloudera
SEPTEMBER 15, 2022
The consumption of the data should be supported through an elastic delivery layer that aligns with demand, but also provides the flexibility to present the data in a physical format that aligns with the analytic application, ranging from the more traditional data warehouse view to a graph view in support of relationship analysis.
Towards Data Science
MARCH 6, 2023
And that’s the target of today’s post — We’ll be developing a data pipeline using Apache Spark, Google Cloud Storage, and Google Big Query (using the free tier) not sponsored. The tools Spark is an all-purpose distributed memory-based data processing framework geared towards processing extremely large amounts of data.
ProjectPro
FEBRUARY 8, 2023
ETL stands for Extract, Transform, and Load, which involves extracting data from various sources, transforming the data into a format suitable for analysis, and loading the data into a destination system such as a data warehouse. ETL developers play a significant role in performing all these tasks.
Data Science Blog: Data Engineering
DECEMBER 23, 2022
Noch konkreter wird der Bedarf an Datenbeschaffung und -aufbereitung in der Business Intelligence, denn diese benötigt für nachhaltiges Reporting feste Strukturen wie etwa ein Data Warehouse. Andere Arten von Datenbanken, sogenannte NoSQL -Datenbanken beruhen auf Dateiformaten, einer Spalten- oder einer Graphenorientiertheit.
Towards Data Science
DECEMBER 15, 2023
Taking a hard look at data privacy puts our habits and choices in a different context, however. Data scientists’ instincts and desires often work in tension with the needs of data privacy and security. Anyone who’s fought to get access to a database or data warehouse in order to build a model can relate.
Data Engineering Podcast
SEPTEMBER 22, 2019
Summary Object storage is quickly becoming the unifying layer for data intensive applications and analytics. Modern, cloud oriented data warehouses and data lakes both rely on the durability and ease of use that it provides. How do you approach project governance and sustainability?
Hevo
MAY 8, 2023
The exponential rate of data generation in every modern business from various SaaS applications, Marketing Channels, etc. has compelled them to move from On-premise databases to Cloud-Based Data Warehouses.
Rockset
MARCH 5, 2021
Understanding the space-time tradeoff in data analytics In computer science, a space-time tradeoff is a way of solving a problem or calculation in less time by using more storage space, or by solving a problem in very little space by spending a long time. However for each query it needs to scan your data.
Knowledge Hut
NOVEMBER 16, 2023
Although the cloud providers ensure for highest level of safety for the data, it still faces a potential risk. As per the reports, Microsoft AI Research division accidentally leaked 38 terabytes of private data via unsecured cloud storage. How Azure and Data Science are Shaping the Future?
Rockset
AUGUST 4, 2021
Organizations that depend on data for their success and survival need robust, scalable data architecture, typically employing a data warehouse for analytics needs. Snowflake is often their cloud-native data warehouse of choice. Snowflake provides a couple of ways to load data.
Knowledge Hut
APRIL 25, 2023
Data engineers add meaning to the data for companies, be it by designing infrastructure or developing algorithms. The practice requires them to use a mix of various programming languages, data warehouses, and tools. While they go about it - enter big data data engineer tools.
Striim
FEBRUARY 10, 2023
Quickly turn data into actionable insights that help Macy’s deliver quality digital customer experiences and improve operational efficiencies. Macy’s migrated its on-premise inventory and order data to Google Cloud storage to reach its objectives. Help customers visualize their data using a business intelligence tool.
Cloudera
JULY 15, 2019
After taking this course, you’ll understand how databases provide structure to data and how this has changed as the volume and variety of data have increased. You’ll compare operational and analytic databases and learn what differentiates a modern distributed data warehouse.
ProjectPro
FEBRUARY 16, 2023
Businesses will be better able to make smart decisions and achieve a competitive advantage if they can successfully integrate data from various sources using SQL. If your database is cloud-based, using SQL to clean data is far more effective than scripting languages. But how does SQL play a vital role here?
Cloudera
SEPTEMBER 10, 2021
Shared Data Experience ( SDX ) on Cloudera Data Platform ( CDP ) enables centralized data access control and audit for workloads in the Enterprise Data Cloud. The public cloud (CDP-PC) editions default to using cloud storage (S3 for AWS, ADLS-gen2 for Azure).
Knowledge Hut
JANUARY 19, 2024
Because it involves analyzing large amounts of data from multiple sources, business analytics can be a time- and resource-intensive process. Tools Business intelligence uses various tools to collect, analyze, and report data. Business analytics uses predictive models to forecast future trends.
Rockset
JANUARY 25, 2022
Use Result Caching Every time you execute a query, it caches, so Snowflake doesn’t need to spend time retrieving the same results from cloud storage in the future. Rockset is an excellent complement to your Snowflake data warehouse. Rockset is the real-time analytics database in the cloud for modern data teams.
ProjectPro
DECEMBER 7, 2021
Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.
Cloudera
FEBRUARY 16, 2022
In the case of CDP Public Cloud, this includes virtual networking constructs and the data lake as provided by a combination of a Cloudera Shared Data Experience (SDX) and the underlying cloud storage. Each project consists of a declarative series of steps or operations that define the data science workflow.
Cloudera
FEBRUARY 7, 2019
ATB Financial also now runs 40 nodes of HDP on its’ Google Cloud Platform (GCP) — as well as an HDF cluster — as an ingest framework to shift data from an on-premises data warehouse into its HDP cloud cluster for storage and processing.
ProjectPro
AUGUST 11, 2021
“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?
Data Engineering Podcast
FEBRUARY 18, 2024
Summary A data lakehouse is intended to combine the benefits of data lakes (cost effective, scalable storage and compute) and data warehouses (user friendly SQL interface). Multiple open source projects and vendors have been working together to make this vision a reality.
Monte Carlo
AUGUST 25, 2023
Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider. So let’s get to the bottom of the big question: what kind of data storage layer will provide the strongest foundation for your data platform?
Rockset
MAY 24, 2021
The most popular data storage layers for a serverless stack include: Amazon S3: Amazon Simple Storage Service is offered through AWS as a scalable infrastructure solution. Azure Data Lake: Microsoft's analytics platform and serverless data lake is offered through the company's public cloud, Azure.
AltexSoft
FEBRUARY 11, 2023
It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);
dbt Developer Hub
NOVEMBER 22, 2022
Once your data warehouse is built out, the vast majority of your data will have come from other SaaS tools, internal databases, or customer data platforms (CDPs). Spreadsheets are the Swiss army knife of data processing. But there’s another unsung hero of the analytics engineering toolkit: the humble spreadsheet.
Rockset
SEPTEMBER 15, 2020
Before the advent of real-time databases, a user would typically use a data pipeline to clean and homogenize all the fields, flatten nested fields, denormalize nested objects and then write it out it to a data warehouse like Redshift or Snowflake. The data warehouse is then used to gather insights from their data.
Knowledge Hut
JUNE 26, 2023
The Structured Streaming API offered by Spark makes it possible for data to be processed in real-time in mini-batches, which in turn offers low-latency processing capabilities. The processed data are uploaded to Google Cloud Storage, where they are then subjected to transformation with the assistance of dbt.
U-Next
SEPTEMBER 7, 2022
The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. There are times when the data is structured , but it is often messy since it is ingested directly from the data source. What is Data Warehouse? . Data Warehouse in DBMS: .
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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