Data ingestion что это
Дата-инженеры в бизнесе: кто они и чем занимаются?
Данные — один из активов организации. Поэтому вполне вероятно, что перед вашей командой в какой-то момент могут возникнуть задачи, которые можно будет решить, используя эти данные разными способами, начиная с простых исследований и вплоть до применения алгоритмов машинного обучения.
И хоть построение крутой модели — неотъемлемо важная часть, но все же это не залог успеха в решении подобных задач. Качество модели в большой степени зависит от качества данных, которые собираются для нее. И если Data Science применяется не ради спортивного интереса, а для удовлетворения реальных потребностей компании, то на это качество можно повлиять на этапе сбора и обогащения данных. И за это отвечает скорее не дата-сайентист, а другой специалист — дата-инженер.
В этой статье я хочу рассказать о роли дата-инженера в проектах, связанных с построением моделей машинного обучения, о зоне его ответственности и влиянии на результат. Разбираемся на примере Яндекс.Денег.
Какие роли есть в Data Science-проекте?
К сожалению, не для всех названий ролей есть аналоги в русском языке. Если у вас в компании есть устоявшееся русское название, например, для Data Ingest, то поделитесь им в комментариях.
Например, можно выделить следующие роли:
Что такое Data Science-проект?
Это ситуация, когда мы пытаемся решить какую-то задачу при помощи данных. То есть во-первых, эта задача должна быть сформулирована. Например, один из наших проектов начался с того, что нам нужно было распознавать аварии в приеме платежей (далее распознавание аварий будет упоминаться как исходная задача).
Во-вторых, должен быть набор конкретных данных, датасет, на котором мы будем пытаться ее решать. Например, есть список операций. Из него можно построить график количества операций по каким-нибудь временным периодам, например, часам:
Сам график с количеством не требует дата-сайенса, но уже требует дата-инженерии.
Не будем забывать, что помимо простых показателей, таких как количество, показатели, которые нас интересуют, могут быть достаточно сложными в получении: например, количество уникальных пользователей или факт наличия аварии в магазине-партнере (который достоверно определять силами человеческого мониторинга весьма дорого).
При этом данных с самого начала может быть много либо их в какой-то момент внезапно становится много, а в реальной жизни — они еще и продолжают непрерывно копиться даже после того, как мы сформировали для анализа какой-то датасет.
Как, наверное, для любой проблемы сначала стоит посмотреть, есть ли на рынке готовые решения. И во многих случаях окажется, что они есть. Например, существуют системы, которые умеют детектить простои тем или иным способом. Однако та же Moira не справлялась полностью с нашими проблемами (из коробки она ориентируется на статические правила — которыми задать наши условия достаточно сложно). Поэтому мы решили писать классификатор самостоятельно.
И дальше в статье рассматриваются те случаи, когда нет готового решения, которое полностью бы удовлетворяло возникшим потребностям, или если даже оно есть, то мы не знаем о нем или оно нам недоступно.
В этот момент из инженерной области, где что-то разрабатываем, мы переходим в RnD-область, где пытаемся изобрести алгоритм или механизм, который будет работать на наших данных.
Порядок действий в DS-проекте
Давайте посмотрим, как это выглядит в реальной жизни. Дата-сайентический проект состоит из следующих этапов:
В проектах, которыми мы занимались, один такой круг занимал по времени около 1,5-2 недель.
Дата-сайентист точно участвует на этапе построения модели и при оценке результата. Все остальные этапы чаще ложатся на плечи дата-инженера.
Теперь рассмотрим этот процесс подробнее.
Сбор датасета
Как мы сказали, без набора данных бессмысленно начинать любой Data Science. Давайте посмотрим, из каких данных получился график с количеством платежей.
В нашей компании применяется микросервисная архитектура, и в ней для дата-инженера наиболее важный момент, что нужные данные еще нигде не собраны воедино. Каждый микросервис льет свои события в брокер, в нашем случае Kafka, ETL оттуда их забирает, кладет в DWH, откуда их забирают модели.
Каждый микросервис знает только свой кусочек: один компонент знает про авторизацию, другой — про реквизиты и так далее. Задача дата-инженера — эти данные собрать в одном месте и объединить их друг с другом, чтобы получился необходимый датасет.
В реальной жизни микросервисы появились неспроста: такой атомарной операции, как платеж, не существует. У нас даже есть такое внутреннее понятие, как процесс платежа — последовательность операций для его выполнения. Например, в эту последовательность могут входить следующие операции:
Действия могут быть как явно существующими в этом процессе, так и суррогатными (расчетными).
И в нашем примере мы решили, что нам будет достаточно знать два следующих шага:
На этом этапе собранные данные уже могут представлять ценность не только для главной задачи. В нашем примере уже здесь без применения ML можно брать количество процессов, прошедших каждый из этих шагов, поделить друг на друга и рассчитывать таким образом success rate.
Но если вернуться к главной задаче, то после того, как мы решили выделить эти два события, следует научиться извлекать данные из этих событий и куда-то их складывать.
На этом этапе важно помнить, что большинство моделей классификаций на входе принимает матрицу признаков (набор m чисел и n столбцов). А события, которые мы получаем, например, из Kafka, — это текст, а не числа, и из этого текста матрицу не составишь. Поэтому изначально текстовые записи нужно преобразовать в числовые значения.
Составление корректного датасета состоит из следующих этапов:
Например, в поле «дата» появился платеж 1970 года, и такую запись, скорее всего, не следует учитывать (если мы в принципе хотим использовать время как признак).
Это можно делать разными способами. Например, полностью исключить строки с неправильными значениями. Это хорошо работает, но могут потеряться остальные данные из этих строк, хотя они могут быть вполне полезными. Или, другой вариант — сделать что-то с неправильными значениями, не трогая остальные поля в этой строчке. Например, заменить на среднее или мат. ожидание по этому полю или вовсе обнулить. В каждом случае принять решение должен человек (дата-сайентист или дата-инженер).
Следующий шаг — разметка. Это тот момент, когда мы помечаем аварии как «аварии». Очень часто это самый дорогостоящий этап в сборе датасета.
Предполагается, что изначально мы знаем откуда-нибудь про аварии. Например, операции идут, затем их количество резко падает (как на картинке выше), а потом они восстанавливаются снова, и кто-то нам говорит: «Вот там и была авария». А дальше нам хочется автоматически находить идентичные кейсы.
Интереснее ситуация, когда операции прекращаются не полностью, а только частично (количество операций не падает до нуля). В этом суть детектинга — отслеживать изменение структуры исследуемых данных, а не их полное отсутствие.
Возможные неточности разметки приводят к тому, что классификатор будет ошибаться. Почему? Например, у нас есть две аварии, а размечена только одна из них. Соответственно, вторую аварию классификатор будет воспринимать как нормальное поведение и не рассматривать как аварию.
В нашем случае мы специально собираем вручную историю аварий, которую потом мы используем в разметке.
В итоге после серий экспериментов одним из решений задачи поиска простоев получился следующий алгоритм:
И не стоит забывать про последний пункт — актуализацию данных. Особенно если проект длинный, готовится несколько недель или месяцев, датасет может устареть. И важно, когда весь пайплайн готов, обновить информацию — выгрузить данные за новый период. Именно в этот момент становится важна роль дата-инженера как автоматизатора, чтобы все предыдущие шаги можно было дешево повторить на новых данных.
Только после этого дата-инженер передает эстафету (вместе с датасетом) дата-сайентисту.
А дальше.
Что же делает дата-сайентист?
Предполагаем, что проблема у нас сформулирована, дальше дата-сайентисту ее нужно решить.
В этой статье я не буду детально затрагивать вопрос выбора модели. Но для тех, кто только начинает работать с ML, отмечу, что есть множество подходов к выбору модели.
Если путем настройки гиперпараметров дата-сайентисту не удалось добиться хорошего качества работы выбранной модели, то нужно выбрать другую модель либо обогатить датасет новыми фичами — значит, требуется пойти на следующий круг и вернуться на этап расчета фич или еще раньше — на этап сбора данных. Угадайте, кто это будет делать?
Предположим, что модель выбрана, отскорена, дата-инженеры оценивают результат и получают обратную связь. Заканчивается ли на этом их работа? Конечно, нет. Приведем примеры.
Сначала немного лирического отступления. Когда я учился в школе, учительница любила спрашивать:
— А если все спрыгнут с крыши, ты тоже спрыгнешь?
Спустя какое-то время я узнал, что для этой фразы есть стандартный ответ:
— Ну… вам же никто не мешает говорить фразу, которую все говорят.
Однако после изобретения машинного обучения ответ может стать более предсказуемым:
— А если все спрыгнут с крыши, ты тоже спрыгнешь?
[изобретено машинное обучение]
— Да!
Такая проблема возникает, когда модель ловит не ту зависимость, которая существует в реальной жизни, а ту, которая характерна только для собранных данных.
Причина, по которой модель ловит не те зависимости, которые есть в реальной жизни, могут быть связаны с переобучением либо со смещением в анализируемых данных.
И если с переобучением дата-сайентист может побороться самостоятельно, то задача дата-инженера в том, чтобы найти и подготовить данные без смещения.
Но кроме смещения и переобучения могут возникнуть и другие проблемы.
Например, когда после сбора данных мы пытаемся на них обучиться, а потом выясняется, что один из магазинов (где проходят платежи), выглядит вот так:
Вот такие у него операции, и все другие наши размышления про падения количества операций, как признака аварии, просто бессмысленны, так как в данном примере есть периоды, где платежей нет совсем. И это нормальный период, тут нет ничего страшного. Что это для нас означает? Это как раз и есть тот случай, когда указанный выше алгоритм не работает.
На практике это частенько означает, что следует перейти к другой проблеме — не той, что мы изначально пытались решать. Например, что-то сделать до того момента, как мы начинаем искать аварии. В рассматриваемой задаче пришлось сначала привести кластеризацию магазинов по профилю: часто платящие, редко платящие, редко платящие со специфическим профилем и другие, но это уже другая история. Но важно, что это, в первую очередь, тоже задача для дата-инженера.
В итоге
Основной вывод, который можно сделать из рассказанного выше, что в реальных ML-проектах дата-инженер играет одну из важных ролей, а возможностей по решению бизнес-задач у него зачастую даже больше, чем у дата-сайентиста.
Если сейчас вы разработчик и хотите развиваться в направлении машинного обучения, то не сосредотачивайтесь исключительно на дата-сайенсе и обратите внимание на дата-инженерию.
Data Ingestion – Definition, Challenges, and Best Practices
Organizations today rely heavily on data for predicting trends, forecasting the market, planning for future requirements, understanding consumers, and making business decisions. But to accomplish these tasks, it is essential to get fast access to enterprise data in one place. This is where data ingestion comes in handy. But what is it? It refers to the extraction of information from disparate sources so that you can uncover insights hidden in your data and use them for business advantage. It is best to partner up with companies that offer efficient data ingestion services for accurate and timely insights.
What is Data Ingestion?
It is defined as the process of absorbing data from a variety of sources and transferring it to a target site where it can be deposited and analyzed. Generally speaking, the destinations can be a database, data warehouse, document store, data mart, etc. On the other hand, there are various source options, such as spreadsheets, web data extraction or web scrapping, in-house apps, and SaaS data.
Enterprise data is usually stored in multiple sources and formats. For example, sales data is stored in Salesforce.com, Relational DBMSs store product information, etc. As this data originates from different locations, it must be cleaned and converted in a form that can be easily analyzed for decision-making using an easy-to-use data ingestion tool. Otherwise, you will be left with puzzle pieces that cannot join together.
Data ingestion can be performed in different ways, such as in real-time, batches, or a combination of both (known as lambda architecture) depending on the business requirements. Let us look at ways to perform data ingestion in more detail.
Data ingestion in real-time, also known as streaming data, is helpful when the data collected is extremely time-sensitive. Data is extracted, processed, and stored as soon as it is generated for real-time decision-making. For example, data acquired from a power grid has to be supervised continuously to ensure power availability.
When ingestion occurs in batches, the data is moved at recurrently scheduled intervals. This approach is beneficial for repeatable processes. For instance, reports that have to be generated every day.
The lambda architecture balances the advantages of the above-mentioned two methods by utilizing batch processing to offer broad views of batch data. Plus, it uses real-time processing to provide views of time-sensitive information.
After understanding what data ingestion means, let’s delve into its benefits and challenges.
Data Ingestion Benefits
Data ingestion has numerous benefits for any organization as it enables a business to make better decisions, deliver improved customer service, and create superior products. In other words, the data ingestion process helps a business gain a better understanding of its audience’s needs and behavior and stay competitive which is why ample research should be done when looking for companies that offer data ingestion services.
Overall, data ingestion is one of the most effective ways to deal with inaccurate, unreliable data.
Challenges Associated with Data Ingestion
The following are the key challenges that can impact data ingestion and pipeline performances:
Writing codes to ingest data and manually creating mappings for extracting, cleaning, and loading data can be cumbersome as data today has grown in volume and become highly diversified.
Therefore, there is a move towards data ingestion automation. The old procedures of ingesting data are not fast enough to persevere with the volume and range of varying data sources. Hence, an advanced data ingestion tool is required to ease the process.
With the constant evolution of new data sources and internet devices, businesses find it challenging to perform data integration to extract value from their data.
This is mainly because of the ability to connect to that data source and cleaning the data acquired from it, like identifying and eliminating faults and schema inconsistencies in data.
Data ingestion can become expensive because of several factors. For example, the infrastructure you need to support the various data sources and patented tools can be very costly to maintain in the long run.
Similarly, retaining a team of data scientists and other specialists to support the ingestion pipeline is also expensive. Plus, you also have the probability of losing money when you can’t make business intelligence decisions quickly.
Security is the biggest challenge that you might face when moving data from one point to another. This is because data is often staged in numerous phases throughout the ingestion process. This makes it challenging to fulfill compliance standards during ingestion.
Incorrectly ingesting data can result in unreliable connectivity. This can disrupt communication and cause loss of data.
Data Ingestion Best Practices
To deal with the challenges associated with data ingestion, we have compiled three best practices to simplify the process:
Anticipate Difficulties and Plan Accordingly
The prerequisite of analyzing data is transforming it into a useable form. As the data volume increases, this part of their job becomes more complicated. Therefore, anticipating the difficulties in the project is essential to its successful completion.
The first step of the data strategy would be to outline the challenges associated with your specific use case difficulties and plan for them accordingly. For instance, identify the source systems at your disposal and ensure you know how to extract data from these sources. Alternatively, you can acquire external expertise or use a code-free data ingestion tool to help with the process.
Automate the Process
As the data is growing both in volume and complexity, you can no longer rely on manual techniques to curate such a huge amount of data. Therefore, consider automating the entire process to save time, increase productivity, and reduce manual efforts.
For instance, you want to extract data from a delimited file stored in a folder, cleanse it, and transfer it into the SQL Server. This process has to be repeated every time a new file is dropped in the folder. Using a data ingestion tool that can automate the process by using event-based triggers can optimize the entire ingestion cycle.
Furthermore, automation offers the additional benefits of architectural consistency, consolidated management, safety, and error management. All this eventually helps in decreasing the data processing time.
Enable Self-Service Data Ingestion
Your business might need several new data sources to be ingested weekly. And if your company works on a centralized level, it can face trouble in executing every request. Therefore, making the ingestion process automated or opting for self-service data ingestion can empower business users to handle the process with minimal intervention from the IT team.
Wrap Up
Hopefully, by now you understand what data ingestion means along with its efficient usage. Additionally, data ingestion tools can help with business decision-making and improving business intelligence. It reduces the complexity of bringing data from multiple sources together and allows you to work with various data types and schema.
Moreover, an efficient data ingestion process can provide actionable insights from data in a straightforward and well-organized method. Practices like automation, self-service data ingestion, and anticipating difficulties can enhance your data ingestion process by making it seamless, fast, dynamic, and error-free.
Explore the data ingestion capabilities of Astera Centerprise by downloading the free trial version.
Ingest data
Some or all of the functionality noted in this topic is available as part of a preview release. The content and the functionality are subject to change.
This topic describes how to ingest data into Microsoft Dynamics 365 Supply Chain Insights.
To generate insights that are relevant to your business, Dynamics 365 Supply Chain Insights requires data that is relevant to your supply chain. Therefore, that data must be brought (ingested) into the application. Supply Chain Insights uses Power Query to help ensure a smooth data ingestion experience.
Prerequisites
Data management requires that you ingest data from various sources, according to the entities that are described in Data entities. For example, this sample Excel file contains data that can be used for the vendor, warehouse, production plant, bill of materials (BOM), and product entities. Although this data might not contain all the attributes for every entity, it will be sufficient, because it includes the required attributes for each entity.
Before ingesting your data, review the information in Compliance to ensure that Supply Chain Insights meets your company’s expectations.
Get started
To start the ingestion process, open the Data import page, and select an entity that has a status other than Not imported. Select Not imported or the vertical ellipsis button, and then select Import data.
Sources
To enter the data for any entity, import a local comma-separated values (.csv) file or Excel (.xlsx) file from your computer, or connect Supply Chain Insights to your own data storage or cloud storage service. In both cases, make sure that your data contains the required attributes of a given entity. For example, if you upload a local file, column headers must be named. For cloud storage, additional information will be required to authenticate Supply Chain Insight’s access to the data, depending on the cloud storage service that you select.
Local file prerequisites
Mappings
Mappings inform Supply Chain Insights how to interpret your data so that it can be analyzed. A mapping describes how your data is related to the attributes that represent a specific entity. It’s easy to complete a mapping during the ingestion process.
Mapping data from local files
Local files that you upload must have column headers, because Supply Chain Insights uses the headers to map your data to the attributes of the entity. If you select Auto map, Supply Chain Insights tries to use the column headers to determine which column represents which attribute. To ensure that automatic mapping is run correctly, select the Mapped attributes column together with the Data preview table at the bottom of the page. If an error occurs, or if you prefer to do the mapping manually, select the option for the required attribute in the Mapped attributes column, and then select the appropriate column header name.
Mapping data from a cloud storage provider
If a table that represents the desired entity is available, select it in the left column after a data source has been selected. If no table is available, the Power Query interface contains numerous tools that you can use to transform your data into a single table that represents the entity. For more information about those tools and how to use them, see Transform data. After a table that contains all the information for an entity has been created, you can have Power Query automatically map the information in your table to the attributes of the entity. Select Map to entity in the upper right, select the entity in the left column of the pop-up window, and then select Auto map. Review the query output column for any errors, or use that column to manually map your data, and then select Done.
Refresh schedule for data ingested through the cloud
Up-to-date insights rely on up-to-date data ingestion. There are three ways to ensure that data ingestion is up to date:
Data Ingestion, Processing and Big Data Architecture Layers
In the era of the Internet of Things and Mobility, with a huge volume of data becoming available at a fast velocity, there must be the need for an efficient Analytics System.
Also, the variety of data is coming from various sources in different formats, such as sensors, logs, structured data from an RDBMS, etc. In the past few years, the generation of new data has drastically increased. More applications are being built, and they are generating more data at a faster rate.
Earlier, Data Storage was costly, and there was an absence of technology which could process the data in an efficient manner. Now the storage costs have become cheaper, and the availability of technology to transform Big Data is a reality.
What is Big Data Technology?
According to the Author Dr. Kirk Borne, Principal Data Scientist, Big Data Definition is Everything, Quantified, and Tracked. Let’s pick that apart –
Advantages of Big Data
D2D Communication Meets Big Data
10 Vs of Big Data
Big Data Architecture & Patterns
The Best Way to a solution is to “Split The Problem.”Big Data Solution can be well understood using Layered Architecture. The Layered Architecture is divided into different Layers where each layer performs a particular function.
This Architecture helps in designing the Data Pipeline with the various requirements of either Batch Processing System or Stream Processing System. This architecture consists of 6 layers which ensure a secure flow of data.
This layer is the first step for the data coming from variable sources to start its journey. Data here is prioritized and categorized which makes data flow smoothly in further layers.
In this Layer, more focus is on the transportation of data from ingestion layer to rest of data pipeline. It is the Layer, where components are decoupled so that analytic capabilities may begin.
In this primary layer, the focus is to specialize the data pipeline processing system, or we can say the data we have collected in the previous layer is to be processed in this layer. Here we do some magic with the data to route them to a different destination, classify the data flow and it’s the first point where the analytic may take place.
Storage becomes a challenge when the size of the data you are dealing with, becomes large. Several possible solutions can rescue from such problems. Finding a storage solution is very much important when the size of your data becomes large. This layer focuses on “where to store such a large data efficiently.”
This is the layer where active analytic processing takes place. Here, the primary focus is to gather the data value so that they are made to be more helpful for the next layer.
The visualization, or presentation tier, probably the most prestigious tier, where the data pipeline users may feel the VALUE of DATA. We need something that will grab people’s attention, pull them into, make your findings well-understood.
Big Data Ingestion Architecture
Data ingestion is the first step for building Data Pipeline and also the toughest task in the System of Big Data. In this layer we plan the way to ingest data flows from hundreds or thousands of sources into Data Center. As the Data is coming from Multiple sources at variable speed, in different formats.
That’s why we should properly ingest the data for the successful business decisions making. It’s rightly said that “If starting goes well, then, half of the work is already done.”
What is Ingestion in Big Data?
Big Data Ingestion involves connecting to various data sources, extracting the data, and detecting the changed data. It’s about moving data — and especially the unstructured data — from where it is originated, into a system where it can be stored and analyzed.
We can also say that Data Ingestion means taking data coming from multiple sources and putting it somewhere it can be accessed. It is the beginning of Data Pipeline where it obtains or import data for immediate use.
Data can be streamed in real time or ingested in batches, When data is ingested in real time then, as soon as data arrives it is ingested immediately. When data is ingested in batches, data items are ingested in some chunks at a periodic interval of time. Ingestion is the process of bringing data into Data Processing system.
Effective Data Ingestion process begins by prioritizing data sources, validating individual files and routing data items to the correct destination.
Challenges in Data Ingestion
As the number of IoT devices increases, both the volume and variance of Data Sources are expanding rapidly. So, extracting the data such that it can be used by the destination system is a significant challenge regarding time and resources. Some of the other problems faced by Data Ingestion are –
That’s why it should be well designed assuring following things –
Data Ingestion Parameters
Big Data Ingestion Key Principles
To complete the process of Data Ingestion, we should use right tools for that and most important that tools should be capable of supporting some of the fundamental principles written below –
Data Serialization in Big Data
Different types of users have various types of data consumer needs. Here we want to share variable data, so we must plan how the user can access data in a meaningful way. That’s why a single image of variable data optimize the data for human readability.
Approaches used for this are –
It’s an RPC Framework containing Data Serialization Libraries.
It can use the specially generated source code to easily write and read structured data to and from a variety of data streams and using a variety of languages.
The more recent Data Serialization format that combines some of the best features which previously listed. Avro Data is self-describing and uses a JSON-schema description. This schema is included with the data itself and natively support compression. Probably it may become a de facto standard for Data Serialization.
Big Data Ingestion Tools
Apache Flume Architecture
Apache Flume is a distributed, reliable, and available service for efficiently collecting, aggregating, and moving large amounts of log data.
It has a straightforward and flexible architecture based on streaming data flows. It is robust and faults tolerant with tunable reliability mechanisms and many failovers and recovery mechanisms.
It uses a simple, extensible data model that allows for an online analytic application.
Functions of Apache Flume
Apache Nifi Overview
Apache Nifi provides an easy to use, the powerful, and reliable system to process and distribute data. Apache NiFi supports robust and scalable directed graphs of data routing, transformation, and system mediation logic. Its functions are –
Integrating Elasticsearch with Logstash
Elastic Logstash is an open source, server-side data processing pipeline that ingests data from a multitude of sources simultaneously transforms it, and then sends it to your “stash, “ i.e., Elasticsearch.
It easily ingests from your logs, metrics, web applications, data stores, and various AWS services and done in continuous, streaming fashion. It can Ingest Data of all Shapes, Sizes, and Sources.
Big Data Pipeline Architecture
In this Layer, more focus is on transportation data from ingestion layer to rest of Data Pipeline. Here we use a messaging system that will act as a mediator between all the programs that can send and receive messages.
Here the tool used is Apache Kafka. It’s a new approach in message-oriented middleware.
Getting Started with Big Data Pipeline
Big Data Pipeline Functions
Data Pipeline Helps in bringing data into your system. It means taking unstructured data from where it is originated into a system where it can be stored and analyzed for making business decisions
Data Pipeline also helps in bringing different types of data together.
Organizing data means an arrangement of data; this arrangement is also made in Data Pipeline.
It’s also one of the processes where we can enhance, clean, improve the raw data.
After improving the useful data, Data Pipeline provides us with the processed data on which we can apply the operations on raw data and can make business decisions accurately.
Need Of Big Data Pipeline
A Data Pipeline is software that takes data from multiple sources and makes it available to be used strategically for making business decisions.
Primarily reasons for the need of data pipeline is because it’s tough to monitor Data Migration and manage data errors. Other reasons for this are below –
Big Data Pipeline Use Cases
Data Pipeline is useful to some roles, including CTOs, CIOs, Data Scientists, Data Engineers, BI Analysts, SQL Analysts, and anyone else who derives value from a unified real-time stream of user, web, and mobile engagement data. So, use cases for data pipeline are given below –
Apache Kafka Overview
It is used for building real-time data pipelines and streaming apps. It can process streams of data in real-time and store streams of data safely in a distributed replicated cluster.
Kafka works in combination with Apache Storm, Apache HBase and Apache Spark for real-time analysis and rendering of streaming data.
Apache Kafka Use Cases
Apache Kafka Features
Apache Kafka Architecture
Apache Kafka System design act as Distributed commit log, where incoming data is written sequentially on disk. There are four main components involved in moving data in and out of Apache Kafka –
Big Data Processing Layer
In the previous layer, we gathered the data from different sources and made it available to go through rest of pipeline.
In this layer, our task is to do magic with data, as now data is ready we only have to route the data to different destinations.
In this main layer, the focus is to specialize Data Pipeline processing system or we can say the data we have collected by the last layer in this next layer we have to do processing on that data.
Big Data Batch Processing System
A simple batch processing system for offline analytics. For doing this tool used is Apache Sqoop.
What is Apache Sqoop?
It efficiently transfers bulk data between Apache Hadoop and structured datastores such as relational databases. Apache Sqoop can also be used to extract data from Hadoop and export it into external structured data stores.
Apache Sqoop works with relational databases such as Teradata, Netezza, Oracle, MySQL, Postgres, and HSQLDB.
Functions of Apache Sqoop
Near Real-Time Processing System
What is Apache Storm?
It is a system for processing streaming data in real time. It adds reliable real-time data processing capabilities to Enterprise Hadoop. Storm on YARN is powerful for scenarios requiring real-time analytics, machine learning and continuous monitoring of operations.
6 Key Features of Apache Storm
What is Apache Spark?
Apache Spark is a fast, in-memory data processing engine with elegant and expressive development APIs to allow data workers to efficiently execute streaming, machine learning or SQL workloads that require fast iterative access to data sets.
With Spark running on Apache Hadoop YARN, developers everywhere can now create applications to exploit Spark’s power, derive insights, and enrich their data science workloads within a single, shared data set in Hadoop.
Real-Time Processing System
What is Apache Flink?
Apache Flink is an open-source framework for distributed stream processing that Provides results that are accurate, even in the case of out-of-order or late-arriving data. Some of its features are –
Apache Flink Use Cases
Big Data Storage Layer
Next, the major issue is to keep data in the right place based on usage. We have relational Databases that were a successful place to store our data over the years.
But with the new big data strategic enterprise applications, you should no longer be assuming that your persistence should be relational.
We need different databases to handle the different variety of data, but using different databases creates overhead. That’s why there is an introduction to the new concept in the database world, i.e., the Polyglot Persistence.
What is Polyglot Persistence?
Polyglot persistence is the idea of using multiple databases to power a single application. Polyglot persistence is the way to share or divide your data into multiple databases and leverage their power together.
It takes advantage of the strength of different database. Here various types of data are arranged in a variety of ways. In short, it means picking the right tool for the right use case.
It’s the same idea behind Polyglot Programming, which is the idea that applications should be written in a mix of languages to take advantage of the fact that different languages are suitable for tackling different problems.
Advantages of Polyglot Persistence –
Big Data Storage Tools
HDFS : Hadoop Distributed File System
Features of HDFS
GlusterFS: Dependable Distributed File System
As we know good storage solution must provide elasticity in both storage and performance without affecting active operations.
Scale-out storage systems based on GlusterFS are suitable for unstructured data such as documents, images, audio and video files, and log files. GlusterFS is a scalable network filesystem.
Using this, we can create large, distributed storage solutions for media streaming, data analysis, and other data- and bandwidth-intensive tasks.
GlusterFS Use Cases
Amazon S3 Storage Service
Big Data Query Layer
It is the layer where active analytic processing takes place. This is a field where interactive queries are necessaries, and it’s a zone traditionally dominated by SQL expert developers. Before Hadoop, we had an insufficient storage due to which it takes long analytics process.
At first, it goes through a Lengthy process, i.e., ETL to get a new data source ready to be stored and after that, it puts the data in database or data warehouse. But now, data analytics became essential step which solved problems while computing such a large amount of data.
Companies from all industries use big data analytics to –
Big Data Analytics Query Tools
Apache Hive is data warehouse infrastructure built on top of Apache Hadoop for providing data summarization, ad-hoc query, and analysis of large datasets.
Data analysts use Hive to query, summarize, explore and analyze that data, then turn it into actionable business insight.
It provides a mechanism to project structure onto the data in Hadoop and to query that data using a SQL — like a language called HiveQL (HQL).
Features of Apache Hive
Spark SQL includes a cost-based optimizer, columnar storage, and code generation to make queries fast.
At the same time, it scales to thousands of nodes and multi-hour queries using the Spark engine, which provides full mid-query fault tolerance.
Spark SQL is a Spark module for structured data processing. Some of the Functions performed by Spark SQL are –
Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. We use Amazon Redshift to load the data and run queries on the data.
We can also create additional databases as needed by running a SQL command. Most important we can scale it from hundred gigabytes of data to a petabyte or more.
It enables you to use your data to acquire new insights for your business and customers. The Amazon Redshift service manages all of the work of setting up, operating and scaling a data warehouse.
These tasks include provisioning capacity, monitoring and backing of the cluster, and applying patches and upgrades to the Amazon Redshift engine.
Presto is an open source distributed SQL query engine for running interactive analytic queries against data sources of all sizes ranging from gigabytes to petabytes.
Presto was designed and written for interactive analytics and approaches and the speed of commercial data warehouses while scaling to the size of organizations like Facebook.
Presto Capabilities
Who Uses Presto?
Data Lake and Data Warehouse
What is Data Warehouse?
A Data Warehouse is a subject-oriented, Integrated, Time-varying, non-volatile collection of data in support of management’s decision-making process.
So, a Data Warehouse is a centralized repository that stores data from multiple information sources and transforms them into a standard, multidimensional data model for efficient querying and analysis.
Difference Between Big Data and Data Warehouse
While comparing, we found that a big data solution is a technology and that data warehousing is an architecture. They are two very different things.
Technology is just that — a means to store and manage large amounts of data. A data warehouse is a way of organizing data so that there are corporate credibility and integrity.
When someone takes data from a data warehouse, that person knows that other people are using the same data for other purposes. There is a basis for reconcilability of data when there is a data warehouse.
What is Data Lake?
It is a new type of cloud-based enterprise architecture that structures data in a more scalable way that makes it easier to experiment with it.
With data lake, incoming data goes into the lake in a raw form or whatever form data source providers, and there we select and organize the data in a raw form. There are no assumptions about the schema of the data; each data source can use whatever scheme it likes.
It’s up to the consumers of that information to make sense of that data for their purposes. The idea is to have a single store for all of the raw data that anyone in an organization might need to analyze.
Commonly people use Hadoop to work on the data in the lake, but the concept is broader than just Hadoop.
Capabilities of Data Lake
Data Lake vs Data Warehouse
Real-Time Data Monitoring, Data Visualization, Big Data Security
This layer focus on Big Data Visualization. We need something that will grab people’s attention, pull them in, make your findings well-understood. That’s why it provides full Business Infographics. Because your findings from your data need the annotation and the bold canvas.
Data Visualization Layer
The data visualization layer often is the thermometer that measures the success of the project. This is the where the data value is perceived by the user. While it’s designed for handling and storing large volumes of data, Hadoop and other tools have no built-in provisions for data visualization and information distribution, leaving no way to make that data easily consumable by end business users.
Tools For Building Data Visualization Dashboards
Custom Dashboards for Data Visualization
Custom dashboards are useful for creating unique overviews that present data differently, For example, you can –
Real-Time Visualization Dashboards
Real-Time Dashboards save, share, and communicate insights. It helps users generate questions by revealing the depth, range, and content of their data stores.
Data Visualization with Tableau
Exploring data sets With Kibana
Introduction to Intelligence Agents
Recommendation Systems
Angular.JS Framework
Understanding React.JS
Useful Features of React
Big Data Security and Data Flow
Security is the primary task of any work. Security should be implemented at all layers of the lake starting from Ingestion, through Storage, Analytics, Discovery, all the way to Consumption. For proving security to data pipeline, few steps are there that are:-
Authentication will verify user’s identity and ensure they are who they say they are. Using the Kerberos protocol provides a reliable mechanism for authentication.
It is the next step to secure data, by defining which dataset can be consulted by the users or services. Access control will restrict users and services to access only that data which they have permission for; they will access all the data.
Encryption and data masking are required to ensure secure access to sensitive data. Sensitive data in the cluster should be secured at rest as well as in motion. We need to use proper Data Protection techniques which will protect data in the cluster from unauthorized visibility.
Another aspect of data security requirement is Auditing data access by users. It can detect the log on & access attempts as well as the administrative changes.
Real-Time Data Monitoring
Data In enterprise systems is like food — it has to be kept fresh. Also, it needs nourishment. Otherwise, it goes wrong and doesn’t help you in making strategic and operational decisions. Just as consuming spoiled food could make you sick, using “spoiled” data may be bad for your organization’s health.
There may be plenty of data, but it has to be reliable and consumable to be valuable. While most of the focus in enterprises is often about how to store and analyze large amounts of data, it is also essential to keep this data fresh and flavorful.
So we can do this? The solution is for monitoring, auditing, testing, managing, and controlling the data. Continuous monitoring of data is an important part of the governance mechanisms.
Apache Flume is useful for processing log data. Apache Storm is desirable for operations monitoring Apache Spark for streaming data, graph processing, and machine learning. Monitoring can happen in data storage layer. It includes following steps for data monitoring:-
These are the techniques to identify the quality of data and the lifecycle of the data through various phases. In these systems, it is important to capture the metadata at every layer of the stack so it can be used for verification and profiling.Talend, Hive, Pig.
Data is considered to be of high quality if it meets business needs and it satisfies the intended use so that it’s helpful in making business decisions successfully. So, understanding the dimension of greatest interest and implementing methods to achieve it is important.
It means implementing various solutions to correct the incorrect or corrupt data.
Policies have to be in place to make sure the loopholes for data loss are taken care of. Identification of such data loss needs careful monitoring and quality assessment processes.
How Can XenonStack Help You?
XenonStack Big Data Solutions can help you at every layer of Big Data Architecture. XenonStack Big Data Services enables enterprises to Build, Manage and deploy Big Data On-Premises, in the Cloud or on Hybrid Cloud Solutions Using Amazon Big Data Solutions, Azure Big Data Solutions and Google Big Data Solutions. XenonStack Big-Data-as-a-Service delivers –
Big Data Infrastructure Solutions
Deploy, Manage, Monitor Big Data Infrastructure on Apache Hadoop and Apache Spark with different storage solutions HDFS, GlusterFS, and Tachyon On-Premises, Hybrid and Public Cloud.
Apache Hadoop & Spark Consulting Services
XenonStack Delivers expert Apache Hadoop and Spark Consulting and Hadoop Support Services. XenonStack offers innovative solutions for Apache Hadoop and Spark and all of its components including — Kafka, Hive, Pig, MapReduce, Spark, HDFS, HBase and more.
Big Data Security Solutions
Big Data Security solution provides authentication, authorization and Audit to Enable Central Security Administration of Apache Hadoop, Apache Spark, HDFS, Hive, Hbase with Apache Knox and Apache Ranger. Secure Mode Cluster deployment of Apache Hadoop and Apache Spark using Kerboses.