Precisely

JANUARY 25, 2024

To avoid burdening mainframe databases with constant I/O instructions and acknowledgments and prevent latency issues, best practices call for the use of event streaming platforms like Kafka, Amazon Kinesis, Rabbit MQ, or others. Real-time or near real-time replication is most common. Best Practice 2. Best Practice 3. Best Practice 5.

Metadata 67

Metadata Data Governance Relational Database Government 67

Data Engineering Podcast

NOVEMBER 20, 2022

Their SDKs make event streaming from any app or website easy, and their extensive library of integrations enable you to automatically send data to hundreds of downstream tools. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

Data Lake 130

Data Lake Data Ingestion MongoDB Scala 130

Data Engineering Podcast

APRIL 24, 2022

Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. How do you maintain feature parity between the Python and Java integrations? How do you maintain feature parity between the Python and Java integrations?

Machine Learning 100

Machine Learning Systems Data Lake Metadata 100

Data Engineering Podcast

DECEMBER 16, 2019

The solution to discoverability and tracking of data lineage is to incorporate a metadata repository into your data platform. The metadata repository serves as a data catalog and a means of reporting on the health and status of your datasets when it is properly integrated into the rest of your tools.

Metadata 100

Metadata PostgreSQL Datasets Data Warehouse 100

Rock the JVM

OCTOBER 1, 2023

At the time of writing this article, gRPC officially supports 11 programming languages which include Python, Java, Kotlin, and C++ to mention but a few. Metadata provides access to read and write metadata values to be exchanged during a call. import cats.effect. import io.grpc. import fs2.Stream orderid , orderReq. import fs2.Stream

Scala 61

Scala Metadata Transportation Java 61

Confluent

FEBRUARY 21, 2019

During a recent talk titled Hunters ATT&CKing with the Right Data , which I presented with my brother Jose Luis Rodriguez at ATT&CKcon, we talked about the importance of documenting and modeling security event logs before developing any data analytics while preparing for a threat hunting engagement. Yeah…I can do that already!

Process 80

Process Kafka Datasets SQL 80

Cloudera

SEPTEMBER 23, 2020

Access audits are mastered centrally in Apache Ranger which provides comprehensive non-repudiable audit log for every access event to every resource with rich access event metadata such as: IP. Both fine-grained access control of database objects and access to metadata is provided. Policy outcome (access or deny).

Database 82

Database Data Lake Metadata Java 82

Cloudera

JULY 14, 2023

In all the use cases we are trying to migrate a table named “events.” They simply read the underlying data (not even full read, they just read the parquet headers) and create corresponding Iceberg metadata files. Query engines (Impala, Hive, Spark) might mitigate some of these problems by using Iceberg’s metadata files.

Metadata 70

Metadata Data Warehouse Big Data Ecosystem Java 70

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. The interface was designed such that a minimal amount of metadata was needed to construct a pipeline object which performs a given capability. One key component is the Analytics Event Abstraction layer.

Machine Learning 115

Machine Learning Building Metadata Kafka 115

Rock the JVM

FEBRUARY 5, 2023

Giannis is a proud alumnus of Rock the JVM, working as a Solutions Architect with a focus on Event Streaming and Stream Processing Systems. Let’s think of a data stream now A stream is basically an unbounded dataset of incoming events, i.e. it has no end. As we keep ingesting new events, they get appended as new rows to the log.

SQL 61

SQL Kafka Metadata Database 61

Data Engineering Podcast

AUGUST 6, 2022

Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Ascend users love its declarative pipelines, powerful SDK, elegant UI, and extensible plug-in architecture, as well as its support for Python, SQL, Scala, and Java.

Machine Learning 100

Machine Learning Database MySQL PostgreSQL 100

Confluent

MARCH 26, 2019

Apache-Kafka ® -based applications stand out for their ability to decouple producers and consumers using an event log as an intermediate layer. This enables choreographed service collaborations, where many components can subscribe to events stored in the event log and react to them asynchronously. Let’s imagine a “Hello, World!”

Kafka 111

Kafka Transportation Metadata Consulting 111

AltexSoft

JUNE 7, 2021

Apache Hadoop is an open-source framework written in Java for distributed storage and processing of huge datasets. A HDFS Master Node, called a NameNode , keeps metadata with critical information about system files (like their names, locations, number of data blocks in the file, etc.) Hadoop vs Spark differences summarized.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Picnic Engineering

SEPTEMBER 8, 2022

Almost all internal services emit events over RabbitMQ. Our pipeline captures these events and sends them to Confluent Cloud. RabbitMQ We have already mentioned that RabbitMQ is used as the main inter-service communication event bus at Picnic. We use the RabbitMQ Source connector for Apache Kafka Connect.

Kafka 52

Kafka Metadata AWS Java 52

AltexSoft

JULY 18, 2023

It has in-memory computing capabilities to deliver speed, a generalized execution model to support various applications, and Java, Scala, Python, and R APIs. When transformations are applied to RDDs, Spark records the metadata to build up a DAG, which reflects the sequence of computations performed during the execution of the Spark job.

Big Data 52

Big Data Data Process Process Hadoop 52

Cloudera

JUNE 16, 2022

For governance and security teams, the questions revolve around chain of custody, audit, metadata, access control, and lineage. Apache Flink is a distributed processing engine for stateful computations ideally suited for real-time, event-driven applications. Meet Laila, a very opinionated practitioner of Cloudera Stream Processing.

Kafka 86

Kafka Manufacturing Data Lake SQL 86

Big Data Tools

JUNE 8, 2022

DataHub 0.8.36 – Metadata management is a big and complicated topic. DataHub is a completely independent product by LinkedIn, and the folks there definitely know what metadata is and how important it is. If you haven’t found your perfect metadata management system just yet, maybe it’s time to try DataHub!

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Big Data Tools

JUNE 8, 2022

DataHub 0.8.36 – Metadata management is a big and complicated topic. DataHub is a completely independent product by LinkedIn, and the folks there definitely know what metadata is and how important it is. If you haven’t found your perfect metadata management system just yet, maybe it’s time to try DataHub!

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Netflix Tech

DECEMBER 21, 2020

We built AutoOptimize to efficiently and transparently optimize the data and metadata storage layout while maximizing their cost and performance benefits. Sometimes Data Engineers write downstream ETLs on ingested data to optimize the data/metadata layouts to make other ETL processes cheaper and faster. Transparency to end-users.

Data Warehouse 81

Data Warehouse Metadata Algorithm Data 81

Confluent

APRIL 10, 2019

Instead of having many point-to-point connections between sites, the Confluent Platform provides an integrated event streaming architecture with frictionless data replication between sites. Useful to know in case of a disaster event). Is replication running optimally or do operators need to scale up the number of Replicator tasks?

Kafka 83

Kafka Metadata Java Cloud 83

LinkedIn Engineering

JANUARY 16, 2024

These records held vital metadata linked to the restriction, including essential timestamps. To tackle this, we engaged closely with the LinkedIn Java team to fine-tune our JVM applications, refining parameters and optimizing internal data structures and code to mitigate latency spikes.

Kafka 84

Kafka Relational Database Java Architecture 84

Cloudera

MARCH 15, 2023

Introduction Many modern application designs are event-driven. An event-driven architecture enables minimal coupling, which makes it an optimal choice for modern, large-scale distributed systems. Send an event to notify other services about the new order. These services might be responsible for checking the inventory (eg.

PostgreSQL 89

PostgreSQL Kafka Database Data 89

AltexSoft

SEPTEMBER 21, 2023

It is developed in Java and built upon the highly reputable Apache Lucene library. Analysis of logs, metrics, and security events. Each document has unique metadata fields like index , type , and id that help identify its storage location and nature. What is Elasticsearch? Real-time behavior modeling with ML.

Engineering 52

Engineering NoSQL Programming Language Java 52

Rock the JVM

JANUARY 21, 2023

Giannis is a proud alumnus of Rock the JVM, working as a Solutions Architect with a focus on Event Streaming and Stream Processing Systems. Introduction Apache Kafka is a well-known event streaming platform used in many organizations worldwide. The producer will send some events to Kafka. EVENTS_TOPIC , event.

Kafka 65

Kafka Java Scala Coding 65

Cloudera

NOVEMBER 13, 2020

Metadata Caching. This is used to provide very low latency access to table metadata and file locations in order to avoid making expensive remote RPCs to services like the Hive Metastore (HMS) or the HDFS Name Node, which can be busy with JVM garbage collection or handling requests for other high latency batch workloads.

Metadata 143

Metadata Coding SQL Database 143

AltexSoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. This scenario involves three main characters — publishers, subscribers, and a message or event broker. In former times, Kafka worked with Java only. Kafka cluster and brokers. Kafka cluster architecture.

Kafka 93

Kafka Hadoop ETL Tools Big Data 93

Netflix Tech

OCTOBER 18, 2022

Steps can have dependencies, triggers, workflow parameters, metadata, step parameters, configurations, and branches (conditional or unconditional). Therefore, on top of manual and time-based triggering, we also provide event-driven triggering. Users can write the code in Java syntax as the parameter definition.

Java 81

Java Data Machine Learning Systems 81

Monte Carlo

MAY 31, 2022

Metadata from the data warehouse/lake and from the BI tool of record can then be used to map the dependencies between the tables and dashboards. We leverage the open source ANTLR parser, which we heavily customized for various dialects of SQL, in a Java-based lambda function to comb through the query logs and generate lineage data.

Data Lake 52

Data Lake Building Scala Metadata 52

Netflix Tech

OCTOBER 16, 2019

Each version contains metadata (keys and values) and a data pointer. You can think of a data pointer as special metadata that points to where the actual data you published is stored. for example Open Connect Appliance cache configuration, supported device type IDs, supported payment method metadata, and A/B test configuration.

Datasets 75

Datasets Metadata Bytes Machine Learning 75

Rockset

SEPTEMBER 14, 2021

Here’s how Rockset is making it easier to ingest event data from Kafka with this new integration: It is managed entirely by Rockset and can be setup with just a few clicks, keeping with our philosophy on making real-time analytics accessible. There is no need to pre-create a schema to run real-time analytics on event streams from Kafka.

Kafka 52

Kafka SQL MongoDB Computer Science 52

Netflix Tech

JULY 30, 2019

Dynamic Workflow Executions For use cases where the need arises to execute a large/arbitrary number of varying workflow definitions or to run a one-time ad hoc workflow for testing or analytical purposes, registering definitions first with the metadata store in order to then execute them only once, adds a lot of additional overhead.

Metadata 75

Metadata Media AWS Transportation 75

ProjectPro

NOVEMBER 22, 2021

StructType is a collection of StructField objects that determines column name, column data type, field nullability, and metadata. To define the columns, PySpark offers the pyspark.sql.types import StructField class, which has the column name (String), column type (DataType), nullable column (Boolean), and metadata (MetaData).

Hadoop 52

Hadoop Python Datasets Metadata 52

Cloudera

JANUARY 21, 2022

System metadata is reviewed and updated regularly. Services in each zone use a combination of kerberos and transport layer security (TLS) to authenticate connections and APIs calls between the respective host roles, this allows authorization policies to be enforced and audit events to be captured. Sensitive data is encrypted.

Architecture 110

Architecture Transportation Certification Government 110

Confluent

MAY 21, 2019

This leads us to event streaming microservices patterns. Now that the profile change event is published, it can be received by the quote service. Now that the profile change event is published, it can be received by the quote service. In fact, schemas are more than just a contract between two event streaming microservices.

Kafka 110

Kafka Insurance Architecture Database 110

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. Such an object storage model allows metadata tagging and incorporating unique identifiers, streamlining data retrieval and enhancing performance.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Confluent

FEBRUARY 22, 2019

We have delivered an event streaming platform which gives strong guarantees on data quality, using Apache Kafka ® and Protocol Buffers. A key requirement of our centralised event streaming platform is resilience and one step towards achieving this is providing guarantees about the structure of messages and data types within those messages.

Kafka 79

Kafka Programming Language Metadata Data 79

ProjectPro

NOVEMBER 15, 2021

It even allows you to build a program that defines the data pipeline using open-source Beam SDKs (Software Development Kits) in any three programming languages: Java, Python, and Go. A Vespa instance is made up of several stateless Java container clusters and one or more data-storing content clusters.

Big Data 52

Big Data Project Metadata Programming Language 52