Towards Data Science

FEBRUARY 9, 2023

This post is for those poor souls that need to scan terabytes of data in BigQuery to calculate some counts, sums, or rolling totals over huge event data on a daily or even at a higher frequency basis. In this post, I will go over a technique for enabling a cheap data injestion and cheap data consumption for “big data”.

Big Data 83

Big Data Raw Data Aggregated Data Data 83

Snowflake

JUNE 8, 2023

Data producers deliver data products from a single source or set of sources, such as data from a CRM application. Those data products could be used by themselves or aggregated into an aggregate data product, like the customer 360 described above. Product thinking works from the outside in.

Aggregated Data 72

Aggregated Data Raw Data Telecommunication Data 72

Picnic Engineering

OCTOBER 31, 2023

Datadog aggregates data based on the specific “operations” they are associated with, such as acting as a server, client, RabbitMQ interaction, database query, or various methods. The capability to aggregate data in one place, combined with a wide range of integrations, simplifies data collection and access.

Java 52

Java Aggregated Data Coding Python 52

Lyft Engineering

JUNE 28, 2023

The Event Driven Decisions capability in particular turned out to be general enough as to be applicable to a wide range of use cases. At the time of writing, a Mapping team is working to utilize theEvent Driven Decisions product to rebuild Lyft’s Traffic infrastructure by aggregating data per geohash and applying a model.

Machine Learning 116

Machine Learning Building Metadata Kafka 116

Cloudera

JUNE 29, 2023

Incorporate data from novel sources — social media feeds, alternative credit histories (utility and rental payments), geo-spatial systems, and IoT streams — into liquidity risk models. Apply predictive-analytic and ML techniques to this data to create more accurate profiles and proactively identify high-risk customers.

Data Architecture 84

Data Architecture Architecture Management Banking 84

Rockset

AUGUST 25, 2021

It’s simply too expensive to store all the raw data and simply too slow to run batch processes to pre-aggregate it. One common example is a mobile app, where every activity is recorded as an event, resulting in millions of events per day streaming in.

Aggregated Data 52

Aggregated Data Hadoop SQL Data Lake 52

Lyft Engineering

NOVEMBER 29, 2023

Our initial use for Druid was for near real-time geospatial querying and high performance on high-cardinality data sets. It also allowed us to optimize for handling time-series data and event data at scale. Pre-aggregating data at ingestion time helped optimize our query performance and reduce our storage costs.

Kafka 104

Kafka Data Ingestion Datasets Architecture 104

Striim

AUGUST 22, 2024

Streaming analytics focuses on analyzing data in motion, unlike traditional analytics, which deals with data stored in databases or data warehouses. Because of this, streaming analytics is especially impactful for fraud detection, log analysis, and sensor data processing use cases.

Data Warehouse 52

Data Warehouse Retail Machine Learning Database 52

Striim

OCTOBER 11, 2024

Exactly-Once Processing (E1P) Data loss and duplication are critical issues in data pipelines that need to be addressed for reliable data processing. Modern pipelines incorporate Exactly-Once Processing (E1P) to ensure data integrity.

Data Pipeline 52

Data Pipeline MongoDB Unstructured Data Data Lake 52

Rockset

JULY 21, 2022

All interactions are streamed in the form of semi-structured events into Firebase’s NoSQL cloud database, where the data, which includes a large number of nested objects and arrays, is ingested. Since we no longer have to set up schemas in advance, we can ingest real-time event streams without interruption into Rockset.

NoSQL 52

NoSQL IT MongoDB SQL 52

Rockset

FEBRUARY 16, 2024

Furthermore, Rockset’s ability to pre-aggregate data at ingestion time reduced the cost of storage and sped up queries, making the solution cost-effective at scale. With Rockset’s flexible data model , the team could easily define new metrics, add new data and onboard customers without significant engineering resources.

Architecture 59

Architecture SQL Data Warehouse Database 59

LinkedIn Engineering

DECEMBER 19, 2023

Our RU framework ensures that our big data infrastructure, which consists of over 55,000 hosts and 20 clusters holding exabytes of data, is deployed and updated smoothly by minimizing downtime and avoiding performance degradation. We needed a deep understanding of system dependencies to ensure a smooth deployment process.

Big Data 92

Big Data Hadoop Metadata Data 92

Precisely

MARCH 12, 2024

How does data enrichment work? Check out the Precisely Data Guide to learn more about our data enrichment offerings and find out why Precisely is a global leader in trusted, curated data. Is data enrichment a one-time event, or an ongoing process? That depends on your objectives.

Insurance 52

Insurance Telecommunication Retail High Quality Data 52

Picnic Engineering

SEPTEMBER 8, 2022

Quick re-cap: the purpose of the internal pipeline is to deliver data from dozens of Picnic back-end services such as warehousing, machine learning models, customers and order status updates. The data is loaded into Snowflake, Picnic’s single source of truth Data Warehouse (DWH). Yet, some messages are destined for the DWH only.

Kafka 52

Kafka Metadata AWS Java 52

Lyft Engineering

SEPTEMBER 27, 2022

The very first version (see Figure 1) was designed to consume events, convert data to ML features, orchestrate model executions, and sync decision variables to their respective services. This pipeline ingests tens of millions of events per second and processes them into machine learning features.

Kafka 52

Kafka Aggregated Data Machine Learning Architecture 52

AltexSoft

OCTOBER 21, 2022

This scenario involves three main characters — publishers, subscribers, and a message or event broker. A publisher (say, telematics or Internet of Medical Things system) produces data units, also called events or messages , and directs them not to consumers but to a middleware platform — a broker. Kafka cluster and brokers.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Knowledge Hut

JANUARY 19, 2024

New Analytics Strategy vs. Existing Analytics Strategy Business Intelligence is concerned with aggregated data collected from various sources (like databases) and analyzed for insights about a business' performance. In contrast, Business Analytics involves an analytical approach to solving problems within a business context.

Business Intelligence 52

Business Intelligence BI Business Analyst Aggregated Data 52

Rockset

AUGUST 30, 2021

You can also optionally use WHERE clauses to filter out data. Since only the aggregated data is now ingested and indexed into Rockset, this technique reduces the compute and storage required to track real-time metrics by a few orders of magnitude. So, late and out-of-order arrivals are trivially simple to deal with in Rockset.

SQL 52

SQL Kafka MongoDB MySQL 52

Striim

JULY 10, 2024

Data transformation includes normalizing data, encoding categorical variables, and aggregating data at the appropriate granularity. Central to this process is the comprehensive analysis of historical disruption data combined with real-time information sourced from GPS tracking, weather reports, and live news feeds.

Management 52

Management Transportation Machine Learning High Quality Data 52

Rockset

NOVEMBER 1, 2022

Step 1: Data Acquisition Elasticsearch is rarely the system of record which means the data in it comes from somewhere else for real-time analytics. Rockset has built-in connectors to stream real-time data for testing and simulating production workloads including Apache Kafka , Kinesis and Event Hubs.

Database-centric 52

Database-centric Pipeline-centric SQL Aggregated Data 52

Confluent

MARCH 28, 2019

At Confluent, we want to help developers understand how to think about event streaming and the opportunities it can create. Educating people on what an event stream looks like is a daunting task. Traditionally, making sense of the data flowing in a distributed event streaming platform is done by charts and graphs of aggregated data.

Kafka 74

Kafka Aggregated Data Engineering Media 74

Rockset

DECEMBER 17, 2020

Overview of the Customer 360 App Our app will make use of real-time data on customer orders and events. We’ll use Rockset to get data from different sources and run analytical queries that power our app in Retool. From there, we’ll create a data API for the SQL query we write in Rockset.

Building 40

Building Aggregated Data SQL Database 40

Cloudera

NOVEMBER 8, 2017

Event prediction. Building a full customer 360 requires aggregating data sets into a single view. Pattern recognition. Anomaly detection. All of these capabilities are driven by machine learning (ML.) And recently, ML has been a hot topic among our clients. Cloudera offers a unified platform for analytics and machine learning.

Machine Learning 40

Machine Learning Aggregated Data Algorithm Data Science 40

Confluent

MARCH 28, 2019

At Confluent, we want to help developers understand how to think about event streaming and the opportunities it can create. Educating people on what an event stream looks like is a daunting task. Traditionally, making sense of the data flowing in a distributed event streaming platform is done by charts and graphs of aggregated data.

Kafka 79

Kafka Aggregated Data Engineering Media 79

Netflix Tech

JULY 12, 2019

Gatekeeper accomplishes its prescribed task by aggregating data from multiple upstream systems, applying some business logic, then producing an output detailing the status of each video in each country. and can achieve orders of magnitude more efficient data access, which opens up many possibilities.

Datasets 73

Datasets Kafka Architecture Computer Science 73

ProjectPro

FEBRUARY 8, 2023

Application programming interfaces (APIs) are used to modify the retrieved data set for integration and to support users in keeping track of all the jobs. Users can schedule ETL jobs, and they can also choose the events that will trigger them. Create schedules or events that will act as job triggers.

AWS 98

AWS Scala Metadata Data Lake 98

Netflix Tech

NOVEMBER 20, 2023

Instead of overwriting past X days of data completely by using a lookback window pattern, user workflows just need to MERGE the change data (including late arriving data) into the target table by processing the ICDC table.

Process 86

Process Data Pipeline Datasets SQL 86

Rockset

MARCH 1, 2023

Then, I used the integration to create a collection, twitter_kinesis_30day , that syncs Twitter data from the Kinesis stream. Collection Creation At collection creation time, I can also create ingest transformations including using SQL rollups to continuously aggregate data.

Architecture 59

Architecture AWS SQL Cloud Storage 59

U-Next

SEPTEMBER 7, 2022

The terms “ Data Warehouse ” and “ Data Lake ” may have confused you, and you have some questions. In the event that they are not the same, what are the difference s? Apache Spark and Hadoop can be used for big data analytics on data lakes. . Conclusion . .

Data Lake 52

Data Lake Data Warehouse Unstructured Data Amazon Web Services 52

DareData

JULY 5, 2023

The architecture of a data lake project may contain multiple components, including the Data Lake itself, one or multiple Data Warehouses or one or multiple Data Marts. The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Ascend.io

SEPTEMBER 14, 2023

Use Case: Storing data with PostgreSQL (example) import psycopg2 conn = psycopg2.connect(dbname="mydb", Tailored libraries like PySpark Streaming and Kafka-Python have made real-time data analysis and event processing a streamlined affair in Python. csv') data_excel = pd.read_excel('data2.xlsx')

Data Engineering 52

Data Engineering Data Engineer Python Engineering 52

Scott Logic

JULY 26, 2023

Knowledge Graphs, to quote the Alan Turing Institute , “organise data from multiple sources, capture information about entities of interest in a given domain or task (like people, places or events), and forge connections between them.” It was hypothesised that in combination with the Knowledge Graph, the LLM (e.g.

Banking 52

Banking Aggregated Data Retail Architecture 52

Knowledge Hut

JANUARY 3, 2024

After thoroughly examining each circumstance, they will share any new knowledge obtained about current cyber threats or vulnerabilities inside their network so that future events can be avoided, if possible, by immediate fixes. It is generally used for data mining and link analysis.

Computer Science 52

Computer Science Certification Data Mining Data Security 52

Confluent

FEBRUARY 6, 2019

This raises the question of how to combine the Python experience of data scientists with the benefits of Apache Kafka as a battle-tested, highly scalable data processing and event streaming platform. Apache Kafka and KSQL for data scientists and data engineers. Rapid prototyping is typically used here.

Machine Learning 107

Machine Learning Python Kafka Java 107

Databand.ai

JULY 10, 2023

Faster issue diagnosis: Aggregating data from multiple sources enables engineers to correlate events more easily when troubleshooting problems, allowing them to resolve issues more quickly and prevent future occurrences through proactive measures such as capacity planning or automated remediation actions based on observed trends.

Data Pipeline 52

Data Pipeline Algorithm Raw Data Data Engineering 52

Knowledge Hut

OCTOBER 13, 2023

Aggregate Data: If you don't need granularity, consider aggregating data before loading it into Power BI to reduce the volume of data. Sort and Filter Early: Apply sorting and filtering in your queries as early as possible to reduce the amount of data transferred and processed.

BI 52

BI Business Analyst Datasets Raw Data 52

AltexSoft

SEPTEMBER 21, 2023

Analysis of logs, metrics, and security events. With Elasticsearch, you can aggregate and analyze large streams of logs, metrics, and security events in near real-time, making it indispensable for system monitoring and security information and event management (SIEM). Real-time behavior modeling with ML.

Engineering 52

Engineering NoSQL Programming Language Java 52