I was actually pretty excited about this. Less code means fewer bugs. The original architecture had been around for at least 18 months and could be simplified significantly with a little bit of work. The AWS Data Pipeline architecture outlined in my previous blog post is just under two years old now.
We had used data pipelines as a way to back up Amazon DynamoDB data to Amazon S3 in case of a catastrophic developer error. However, with DynamoDB point-in-time recovery we have a better, native mechanism for disaster recovery.
Additionally, with data pipelines we still own the operations associated with the clusters themselves, even if they are transient. A common challenge is keeping our clusters up with recent releases of Amazon EMR to help mitigate any outstanding bugs.Importing CSV files from S3 into Redshift with AWS Glue
AWS Glue makes sure that every top-level attribute makes it into the schema, no matter how sparse your attributes are as discussed in the DynamoDB documentation. We pass that data frame to a DynamicFrameWriter that writes the table to S3 in the specified format.
Most teams at Amazon own applications that have multiple DynamoDB tables, including my own team.
How to export an Amazon DynamoDB table to Amazon S3 using AWS Step Functions and AWS Glue
Our current application uses five primary tables. Ideally, at the end of an export workflow you can write simple, obvious queries across a consistent view of your tables. However, each exported table is partitioned by the timestamp from when the table was exported. This means that you can interact with your DynamoDB exports as if they were one sane view of your exported DynamoDB tables.
The common stack contains shared infrastructure, and you need only one of these per AWS Region. The table stacks are designed in such a way that you can create one per table-format combination in any given AWS Region.
The common stack contains the majority of the infrastructure. That includes the Step Functions state machine and Lambda functions to trigger and check the state of asynchronous jobs.
It also includes IAM roles that the export stacks use, and the S3 bucket to store the exports. Otherwise, feel free to point this CloudFormation stack at your favorite DynamoDB table that is using provisioned throughput. Tables that use on-demand throughput are not currently supported.
However, the default limit for concurrent runs for an AWS Glue job is three. This is a soft limit, but with this architecture you have headroom to export up to 25 tables before asking for a limit increase. There are a few ways to follow along with the execution.
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As steps are entered and exited, entries are added to the Execution event history list. This is a great way to see what state event in Lambda speak is passed to each step, in case you need to debug. You can also expand the Visual workflow. To showcase how having a separate view table of the most recent snapshot of a table is useful, I use the Reviews table from the previous blog post.In this blog I compare options for real-time analytics on DynamoDB - Elasticsearch, Athena, and Spark - in terms of ease of setup, maintenance, query capability, latency.
There is limited support for SQL analytics with some of these options. I also evaluate which use cases each of them are best suited for. Developers often have a need to serve fast analytical queries over data in Amazon DynamoDB, to enable live views of the business and application features such as personalization and real-time user feedback.
However, as an operational data store optimized for transaction processing, DynamoDB is not well-suited to delivering real-time analytics.
As part of this effort, I spent a significant amount of time evaluating the methods developers use to perform analytics on DynamoDB data and understanding which method is best suited based on the use case and found that Elasticsearch, Athena, and Spark each have their own pros and cons. It is central to many modern applications in ad tech, gaming, IoT, and financial services. While NoSQL databases like DynamoDB generally have excellent scaling characteristics, they support only a limited set of operations that are focused on online transaction processing.
This makes it difficult to develop analytics directly on them. In order to support analytical queries, developers typically use a multitude of different systems in conjunction with DynamoDB. In the following sections, we will explore a few of these approaches and compare them along the axes of ease of setup, maintenance, query capability, latency, and use cases they fit well.
If you want to support analytical queries without encountering prohibitive scan costs, you can leverage secondary indexes in DynamoDB which supports a limited type of queries. However for a majority of analytic use cases, it is cost effective to export the data from DynamoDB into a different system like Elasticsearch, Athena, Spark, Rockset as described below, since they allow you to query with higher fidelity.
One approach is to extract, transform, and load the data from DynamoDB into Amazon S3, and then use a service like Amazon Athena to run queries over it. Amazon Athena expects to be presented with a schema in order to be able to run SQL queries on data in S3.
Therefore, we need to extract the data and compute a schema based on the data types observed in the DynamoDB table. Crawler is a service that connects to a datastore such as DynamoDB and scans through the data to determine the schema. Once both these processes have completed, we can fire up Amazon Athena and run queries on the data in DynamoDB. This entire process does not require provisioning any servers or capacity, or managing infrastructure, which is advantageous. It can be automated fairly easily using Glue Triggers to run on a schedule.
Amazon Athena can be connected to a dashboard such as Amazon QuickSight that can be used for exploratory analysis and reporting. A major disadvantage of this method is that the data cannot be queried in real time or near real time.
Dumping all of DynamoDB's contents can take minutes to hours before it is available for running analytical queries. There is no incremental computation that keeps the two in sync—every load is an entirely new sync.
This also means the data that is being operated on in Amazon Athena could be several hours out of date. The ETL process can also lose information if our DynamoDB data contains fields that have mixed types across different items. Field types are inferred when Glue crawls DynamoDB, and the dominant type detected will be assigned as the type of a column. Although there is JSON support in Athena, it requires some DDL setup and management to turn the nested fields into columns for running queries over them effectively.
This approach can work well for those dashboards and analytics that do not require querying the latest data, but instead can use a slightly older snapshot. Amazon Athena's SQL query latencies of seconds to minutes, coupled with the large end-to-end latency of the ETL process, makes this approach unsuitable for building operational applications or real-time dashboards over DynamoDB.
Once our cluster is set up, we can log into our master node and specify an external table in Hive pointing to the DynamoDB table that we're looking to query.Since it is serverless, you do not have to worry about the configuration and management of your resources. Since the crawler is generated, let us create a job to copy data from DynamoDB table to S3.
For the scope of this article, let us use Python. Refer AWS documentation to know more about the limitations. EC2 instances, EMR cluster etc. However, considering AWS Glue on early stage with various limitations, Glue may still not be the perfect choice for copying data from Dynamodb to S3. HiIt was a really simple and helpful blog. I have 2 questions :- 1 How to deal with the updates to the dynamodb table because we might have to run it at certain intervals and have only the updates data instead of having the whole data imported again.
DynamoDB Use-cases: Dynamodb is heavily used in e-commerce since it stores the data as a key-value pair with low latency. Due to its low latency, Dynamodb is used in serverless web applications. Using S3, data lake can be built to perform analytics and as a repository of data. S3 can be used in Machine Learning, Data profiling etc. Exporting data from DynamoDB to S3.
Pick the table CompanyEmployeeList from tables drop-down list. Let the table info gets created through crawler. Set up crawler details in the window below. Add database name and DynamoDB table name. You can schedule the crawler. For this illustration, it is running on demand as the activity is one-time.
Review the crawler information. Run the crawler. Check the catalog details once crawler is executed successfully.We will see how this can be achieved by using serverless architecture.
S3 bucket should be created to receive data. You point your crawler at a data store, and the crawler creates table definitions in the Data Catalog. Once data is available in S3 bucket then run step 5 to run crawler on this S3 to create database schema for Athena queries. Run queries and create views. Your source code is in bitbucket and your bitbucket setting requires whitelisting of server IP.
You want to clone the repo on bastion server. You can install a Kubernetes cluster on AWS using a tool called kops. Agent forwarding is a mechanism whereby an SSH client allows an SSH server to use the local agent on the server, the user logs into, as if it was local there.
You can create kubernetes cluster using kops command in your existing VPC and hosted zone. Kops will create rest of the required AWS resources.
S3 bucket to store data from Kinesis Firehose is created. You point your crawler at a data store DynamoDB tableand the crawler creates table definitions in the Data Catalog. This table schema definition will be used by Kinesis Firehose delivery Stream later.
DynamoDB Streams is the data source. AWS Lambda invokes a Lambda function synchronously when it detects new stream records. Firehose is configured to deliver data it receives into S3 bucket. Firehose delivers all transformed records into an S3 bucket in Apache Parquet output format.
Run another AWS Glue crawler pointing to data store S3 bucket to create table definition based on the S3 partitioned data. How to associate elastic IP to Bastion server created by using kops cluster.
How to install kops, kubectl using additionalUserData in kops cluster. PuTTY Agent forwarding to connect to private k8s cluster node from laptop via Bastion Agent forwarding is a mechanism whereby an SSH client allows an SSH server to use the local agent on the server, the user logs into, as if it was local there.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
This is the primary method used by most AWS Glue users. A crawler can crawl multiple data stores in a single run. Upon completion, the crawler creates or updates one or more tables in your Data Catalog. The ETL job reads from and writes to the data stores that are specified in the source and target Data Catalog tables.
The role you pass to the crawler must have permission to access Amazon S3 paths and Amazon DynamoDB tables that are crawled. For JDBC connections, crawlers use user name and password credentials. When you define an Amazon S3 data store to crawl, you can choose whether to crawl a path in your account or another account. A table is created for one or more files found in your data store.
If all the Amazon S3 files in a folder have the same schema, the crawler creates one table. Also, if the Amazon S3 object is partitioned, only one metadata table is created. If the data store that is being crawled is a relational database, the output is also a set of metadata tables defined in the AWS Glue Data Catalog. When you crawl a relational database, you must provide authorization credentials for a connection to read objects in the database engine. Depending on the type of database engine, you can choose which objects are crawled, such as databases, schemas, and tables.
The metadata tables that a crawler creates are contained in a database when you define a crawler. If your crawler does not define a database, your tables are placed in the default database. In addition, each table has a classification column that is filled in by the classifier that first successfully recognized the data store. If the file that is crawled is compressed, the crawler must download it to process it. When a crawler runs, it interrogates files to determine their format and compression type and writes these properties into the Data Catalog.
Some file formats for example, Apache Parquet enable you to compress parts of the file as it is written. For these files, the compressed data is an internal component of the file, and AWS Glue does not populate the compressionType property when it writes tables into the Data Catalog.
Connect to Amazon DynamoDB Data in AWS Glue Jobs Using JDBC
In contrast, if an entire file is compressed by a compression algorithm for example, gzipthen the compressionType property is populated when tables are written into the Data Catalog. The crawler generates the names for the tables that it creates. If your crawler runs more than once, perhaps on a schedule, it looks for new or changed files or tables in your data store. The output of the crawler includes new tables and partitions found since a previous run. When an AWS Glue crawler scans Amazon S3 and detects multiple folders in a bucket, it determines the root of a table in the folder structure and which folders are partitions of a table.
The name of the table is based on the Amazon S3 prefix or folder name. You provide an Include path that points to the folder level to crawl. When the majority of schemas at a folder level are similar, the crawler creates partitions of a table instead of two separate tables. To influence the crawler to create separate tables, add each table's root folder as a separate data store when you define the crawler.This will navigate you to Accenture. The data processing workflow typically involves data acquisition, storage, transformation and analysis.
Up to now, AWS has offered a powerful combination of services to support this critical process:. Recently AWS rounded out its data processing services with AWS Gluea fully managed extract, transform and load ETL service to help customers build data warehouses or move and refactor data from one data store to another.
Build, configure and manage their ETL solutions. Maximize capacity usage across multiple environments. AWS Glue is built on top of Apache Sparkwhich provides the underlying engine to process data records and scale to provide high throughput, all of which is transparent to AWS Glue users. The new service has three main components:.
Data Catalog— A common location for storing, accessing and managing metadata information such as databases, tables, schemas and partitions. Developers can customize this code based on validation and transformation requirements.
Scheduler— Once the ETL job is created, it can be scheduled to run on-demand, at a specific time or upon completion of another job. AWS Glue provides a flexible and robust scheduler that can even retry the failed jobs. Examples include data exploration, data export, log aggregation and data catalog. As a part of its journey to cloud, an eCommerce company successfully moves its applications and databases to AWS Cloud.
One of the major workloads is Oracle databases underlying their custom applications. The figure below shows the overall architecture that the company is moving toward. To perform big data processing on data coming from Amazon Aurora and other data sources including Amazon S3, the company would not have to maintain an Apache Hive metastore.
Currently, ETL jobs running on the Hadoop cluster join data from multiple sources, filter and transform the data, and store it in data sinks such as Amazon Redshift and Amazon S3. The same Hadoop cluster is used for batch processing and machine learning on data coming from other sources such as web application logs and cookies, device data, social media content, etc.
However, using Apache Spark as a fast data processing solution on a self-managed Apache Hadoop cluster requires the company to spend considerable time and resources to maintain on Amazon EC2. Moving ETL processing to AWS Glue can provide companies with multiple benefits, including no server maintenance, cost savings by avoiding over-provisioning or under-provisioning resources, support for data sources including easy integration with Oracle and MS SQL data sources, and AWS Lambda integration.
Intelligent crawlers are available out of the box for many AWS services to infer the schema automatically. Customers can write their own classifiers to customize the crawler behavior.
Connections to the following file-based and relational data stores are supported out of the box:. AWS Glue does not directly support crawlers in on-premises data sources. For data sources not currently supported, customers can use Boto3 preinstalled in ETL environment to connect to these services using standard API calls through Python.
AWS might make connectors for more data sources available in future. AWS Glue is available in us-east-1, us-east-2 and us-west-2 region as of October Start now and build your first ETL workflow today. AWS has made available various data sets publicly and provided sample ETL code on github to get you started quickly.If you've got a moment, please tell us what we did right so we can do more of it.
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You might do this if you want to create an archive of data in your DynamoDB table. For example, suppose you have a test environment where you need to work with a baseline set of test data in DynamoDB. You can copy the baseline data to an Amazon S3 bucket, and then run your tests.
Afterward, you can reset the test environment by restoring the baseline data from the Amazon S3 bucket to DynamoDB. You can use this bucket for the examples in this section, if you know the root path for the bucket:. Make a note of the root path of the bucket. The naming convention is:. For these examples, we will use a subpath within the bucket, as in this example:.
Each field is separated by an SOH character start of heading, 0x Create an external table pointing to the unformatted data in Amazon S3.
If you want to specify your own field separator character, you can create an external table that maps to the Amazon S3 bucket. You might use this technique for creating data files with comma-separated values CSV. Create a Hive external table that maps to Amazon S3. When you do this, ensure that the data types are consistent with those of the DynamoDB external table. You can copy data from DynamoDB in a raw format and write it to Amazon S3 without specifying any data types or column mapping.
Create an external table associated with your DynamoDB table. There is no dynamodb. The following steps are written with the assumption you have copied data from DynamoDB to Amazon S3 using one of the procedures in this section. If you are currently at the Hive command prompt, exit to the Linux command prompt.