All Things Distributed

AUGUST 18, 2011

Driving down the cost of Big-Data analytics. The Amazon Elastic MapReduce (EMR) team announced today the ability to seamlessly use Amazon EC2 Spot Instances with their service, significantly driving down the cost of data analytics in the cloud. However, this cannot be done without efficient, scalable data analytics.

Big Data 94

Big Data Analytics AWS Cloud 94

Highly Scalable

MARCH 1, 2012

NoSQL databases are often compared by various non-functional criteria, such as scalability, performance, and consistency. At the same time, NoSQL data modeling is not so well studied and lacks the systematic theory found in relational databases. Document databases advance the BigTable model offering two significant improvements.

Database 279

Database Ecommerce Efficiency Engineering 279

Dynatrace

APRIL 20, 2023

With more automated approaches to log monitoring and log analysis, however, organizations can gain visibility into their applications and infrastructure efficiently and with greater precision—even as cloud environments grow. “The weakness of a data lake is they fail when you need to access them fast,” Pawlowski said.

Analytics 246

Analytics Infrastructure Storage Architecture 246

The Netflix TechBlog

OCTOBER 27, 2020

Data scientists and engineers collect this data from our subscribers and videos, and implement data analytics models to discover customer behaviour with the goal of maximizing user joy. The processed data is typically stored as data warehouse tables in AWS S3. Moving data with Bulldozer at Netflix.

Latency 252

Latency Storage Big Data Tuning 252

The Netflix TechBlog

JULY 26, 2021

At Netflix Studio, teams build various views of business data to provide visibility for day-to-day decision making. With dependable near real-time data, Studio teams are able to track and react better to the ever-changing pace of productions and improve efficiency of global business operations using the most up-to-date information.

Big Data 257

Big Data Government Processing Analytics 257

Dynatrace

FEBRUARY 16, 2023

As teams try to gain insight into this data deluge, they have to balance the need for speed, data fidelity, and scale with capacity constraints and cost. To solve this problem, Dynatrace launched Grail, its causational data lakehouse , in 2022. And without the encumbrances of traditional databases, Grail performs fast. “In

Analytics 246

Analytics Innovation Metrics Database 246

Dynatrace

JUNE 1, 2020

The variables that can impact the performance of an application vary; from coding errors or ‘bugs’ in the software, database slowdowns, hosting and network performance, to operating system and device type support. And I’m sure we’ve all experienced frustration when an application crashes, is slow to load, or doesn’t load at all.

Artificial Intelligence 254

Artificial Intelligence Social Media Monitoring IoT 254

Scalegrid

FEBRUARY 11, 2025

Choosing the right database often comes down to MongoDB vs MySQL. This article will help you understand the core differences in data structure, scalability, and use cases. Whether you need a relational database for complex transactions or a NoSQL database for flexible data storage, weve got you covered.

Scalability 147

Scalability Database Storage IoT 147

Scalegrid

FEBRUARY 8, 2024

At its core, a distributed storage system comprises three main components: a controller for managing the system’s operations, an internal datastore where information is held, and databases geared towards ensuring scalability, partitioning capabilities, and high availability for all types of data.

Storage 130

Storage Systems Big Data Azure 130

The Netflix TechBlog

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. This article will list some of the use cases of AutoOptimize, discuss the design principles that help enhance efficiency, and present the high-level architecture.

Storage 212

Storage Latency Efficiency Data Engineering 212

Dynatrace

MARCH 11, 2021

I took a big-data-analysis approach, which started with another problem visualization. This is required for understanding how I intend to improve the efficiency of (manual) alert ticket handling. The raw event and problem data from Dynatrace for analysis stored in InfluxDB. But that didn’t work for me.

Tuning 264

Tuning Architecture Monitoring Big Data 264

Dynatrace

JUNE 1, 2020

The variables that can impact the performance of an application vary; from coding errors or ‘bugs’ in the software, database slowdowns, hosting and network performance, to operating system and device type support. And I’m sure we’ve all experienced frustration when an application crashes, is slow to load, or doesn’t load at all.

Monitoring 173

Monitoring Performance Social Media Artificial Intelligence 173

Scalegrid

MARCH 28, 2024

Snapshots provide point-in-time captures of the dataset, which are efficient for recovery on startup. On the other hand, an append-only file ensures data safety by recording every write operation that modifies the dataset, allowing for complete data reconstruction in the event of a restart. Data transfer technology.

Cache 130

Cache Storage Scalability Architecture 130

The Netflix TechBlog

FEBRUARY 16, 2021

Operational Efficiency: The majority of the changes require metadata configuration files and library code changes, usually taking days of testing and service release to adopt the updates. SKUDB: SKU catalog data was migrated from the metadata configuration files to a relational database. Persistence Layer?—?SKUDB: What’s Next?

Mobile 218

Mobile Engineering Infrastructure Scalability 218

Highly Scalable

MAY 1, 2012

On the other hand, when one is interested only in simple additive metrics like total page views or average price of conversion, it is obvious that raw data can be efficiently summarized, for example, on a daily basis or using simple in-stream counters. what is the cardinality of the data set)? bits per unique value. References.

Analytics 191

Analytics Traffic Big Data Efficiency 191

The Netflix TechBlog

OCTOBER 18, 2022

by Jun He , Akash Dwivedi , Natallia Dzenisenka , Snehal Chennuru , Praneeth Yenugutala , Pawan Dixit At Netflix, Data and Machine Learning (ML) pipelines are widely used and have become central for the business, representing diverse use cases that go beyond recommendations, predictions and data transformations.

Java 211

Java Scalability Traffic Architecture 211

Percona

JANUARY 19, 2023

Some startups adopted MySQL in its early days such as Facebook, Uber, Pinterest, and many more, which are now big and successful companies that prove that MySQL can run on large databases and on heavily used sites. For instance, in Percona Managed Services , we have many clients with TBs worth of data that are well performant.

Open Source 111

Open Source Storage Database Big Data 111

The Morning Paper

OCTOBER 26, 2020

On the surface this is a paper about fast data ingestion from high-volume streams, with indexing to support efficient querying. Helios also serves as a reference architecture for how Microsoft envisions its next generation of distributed big-data processing systems being built. PVLDB’20.

Cloud 104

Cloud Big Data Latency Architecture 104

All Things Distributed

SEPTEMBER 5, 2013

Over the past few years, two important trends that have been disrupting the database industry are mobile applications and big data. The explosive growth in mobile devices and mobile apps is generating a huge amount of data, which has fueled the demand for big data services and for high scale databases.

Big Data 78

Big Data Mobile Latency Database 78

Scalegrid

MARCH 14, 2024

In practice, a hybrid cloud operates by melding resources and services from multiple computing environments, which necessitates effective coordination, orchestration, and integration to work efficiently. Tailoring resource allocation efficiently ensures faster application performance in alignment with organizational demands.

Strategy 130

Strategy Cloud Artificial Intelligence Infrastructure 130

ScaleOut Software

JULY 19, 2021

Incoming data is saved into data storage (historian database or log store) for query by operational managers who must attempt to find the highest priority issues that require their attention. The best they can usually do in real-time using general purpose tools is to filter and look for patterns of interest.

IoT 78

IoT Big Data Analytics Architecture 78

All Things Distributed

NOVEMBER 15, 2010

Now that our ability to generate higher and higher clock rates has stalled and CPU architectural improvements have shifted focus towards multiple cores, we see that it is becoming harder to efficiently use these computer systems. a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applications.

AWS 125

AWS Programming Latency Architecture 125

The Morning Paper

MAY 14, 2019

Seer: leveraging big data to navigate the complexity of performance debugging in cloud microservices Gan et al., Seer uses a lightweight RPC-level tracing system to collect request traces and aggregate them in a Cassandra database. ASPLOS’19.

Big Data 45

Big Data Cloud Performance Hardware 45

All Things Distributed

JUNE 29, 2011

AWS also applies the same customer oriented pricing strategy: as the AWS platform grows, our scale enables us to operate more efficiently, and we choose to pass the benefits back to customers in the form of cost savings. a Fast and Scalable NoSQL Database Service Designed for Internet Scale Applications. Expanding the Cloud â??

AWS 84

AWS Retail Innovation Strategy 84

Abhishek Tiwari

DECEMBER 27, 2017

ETL refers to extract, transform, load and it is generally used for data warehousing and data integration. ETL is a product of the relational database era and it has not evolved much in last decade. There are several emerging data trends that will define the future of ETL in 2018. Machine learning meets data integration.

Big Data 49

Big Data Artificial Intelligence Storage Hardware 49

Highly Scalable

JUNE 5, 2012

Intersection of sorted lists is a cornerstone operation in many applications including search engines and databases because indexes are often implemented using different types of sorted structures. When this short mask of common elements is obtained, we have to efficiently copy out common elements. in this article.

C++ 102

C++ Java Performance Testing Efficiency 102

The Morning Paper

JUNE 23, 2019

Coupled with stateless application servers to execute business logic and a database-like system to provide persistent storage, they form a core component of popular data center service archictectures. We’ve seen similar high marshalling overheads in big data systems too.) Fetching too much data in a single query (i.e.,

Cache 79

Cache Latency Google Network 79

Testsigma

MAY 7, 2020

IBM Big Data and Analytics Hub website cited a case study, where a US insurance company was estimating 15% of their testing efforts to be just test data collection for the backend system and the frontend system. For testing purposes, usually, a mix of static and dynamic data is needed. Copy production data i.

Testing 60

Testing Storage Database Processing 60

All Things Distributed

DECEMBER 13, 2009

The broad Amazon EC2 customer base brings such diversity in workload and utilization patterns that it allows us to operate Amazon EC2 with extreme efficiency. A highly efficient purchasing model such as Spot Instances is another way in which Amazon EC2 customers benefit from the unique economies of scale found in AWS Infrastructure Services.

Cloud 61

Cloud AWS Storage Innovation 61

The Morning Paper

SEPTEMBER 19, 2019

MongoDB is an important database, and this paper explains the tunable (per-operation) consistency models that MongoDB provides and how they are implemented under the covers. Microsoft have a paper describing their new recovery mechanism in Azure SQL Database , the key feature being that it can recovery in constant time.

Blockchain 46

Blockchain Hardware Google Speed 46

ScaleOut Software

NOVEMBER 24, 2020

Rapid advances in the telematics industry have dramatically boosted the efficiency of vehicle fleets and have found wide ranging applications from long haul transport to usage-based insurance. Using a database, dispatchers can query raw telemetry to determine the information they need to manage the fleet in real time.

Analytics 52

Analytics Architecture Scalability Software Architecture 52

Highly Scalable

MARCH 10, 2015

However, there are a number of other important applications: Manufacturer-sponsored discounts can fall into this category because a retailer is not concerned about the cost of the incentives (covered by the manufacturer), only about efficient targeting. WE07] Evolving Classifiers – Evolutionary Algorithms in Data Mining, T. Zapf, 2007.

Retail 152

Retail C++ Analytics Metrics 152

Testsigma

JULY 14, 2020

Test data storage can be achieved by any of the below options-. Database tables. Tools/ frameworks for data-driven automation testing-. The result will be a very few defects in the production environment because all the possible data is already tested and issues have been fixed accordingly. Time-efficient.

Testing 63

Testing Artificial Intelligence DevOps Big Data 63

KeyCDN

OCTOBER 2, 2018

It’s awesome for discovering how grid systems, CSS animation, Big Data, etc all play roles in real-world web design. Subjects like version control, crowdfunding, database selection and code editor choices are essential to efficient modern workflows, and this is a good place to start learning about them.

Development 75

Development Website Design Code 75

O'Reilly

FEBRUARY 4, 2025

Big data, web services, and cloud computing established a kind of internet operating system. As AI gets more efficient and accessible, we will see its use skyrocket, turning it into a commodity we just can't get enough of. We still have databases, but they went from ACID to NoSQL. Jevons paradox strikes again!

Programming 145

Programming Google Infrastructure Internet 145

Percona

JANUARY 14, 2019

What if we use ClickHouse (which is a columnar analytical database) as our main datastore? Well, typically, an analytical database is not a replacement for a transactional or key/value datastore. However, ClickHouse is super efficient for timeseries and provides “sharding” out of the box (scalability beyond one node).

Database 47

Database Analytics Blockchain Programming 47