#Redshift rename table update#
This makes syncing our dataset to Redshift challenging, because it means we might need to update an event to another user (e.g., “unidentified user clicked share button” needs to get updated to “user with identity clicked share button”). For example, you might use this to analyze users that sign up for a product on the web and determine what percentage later activate a mobile app.
#Redshift rename table full#
This gives our customers a full view of their users’ interaction with their products, across different cookies and devices. If a customer calls heap.identify with the same email address for two users, we’ll combine them into a single user record. Heap provides an API – heap.identify – that lets customers tag users with global identities (often an email address). Why Heap’s Dataset is Very Difficult to Sync More information about the Heap SQL schema can be found in our docs.
This is a more useful representation for some analyses that our users care about, such as figuring out what commonly happens immediately before a conversion event. One all_events table, which is effectively a concatenation of all of the event tables. One users table, containing a column for every user-level property Heap captures and another for every custom user property provided via our API.Ī table for each event defined in Heap or logged via our API, with a column for every event property. We organize a customer’s data in Redshift as follows: However, the differences aren’t exposed in the query language, which can lead to a false sense of security for users familiar with Postgres. These differences need to be taken into account to design tables and queries for optimal performance.
This makes batch inserts fast, but makes it easy to accidentally cause data quality issues via duplication or foreign key violations. Instead, each table has a user-specified sort key, which determines how rows are ordered.** The query planner uses this information to optimize queries.Ĭonstraints aren’t enforced – Redshift doesn’t enforce primary or foreign key constraints. It doesn’t support indexes – You can’t define indexes in Redshift. Each table has a user-specified distribution key, which determines how rows in the table are sharded across compute nodes. It’s distributed – A Redshift cluster consists of several compute nodes orchestrated by one leader node. Column stores have much better I/O characteristics for analytical workloads (large joins involving a small number of columns, batch inserts), but are typically slower for transactional workloads(lots of small inserts and updates). This means it stores table data organized in terms of columns, rather than rows, so a query that touches a small number of columns on a table can read the columns that are relevant and ignore the rest. It exposes a Postgres-like interface, but under the hood it’s different in a couple ways:ĭata is stored in columns – Unlike Postgres, Redshift is a column store. Redshift is a cloud-based data warehouse offered by Amazon. This blog post describes some of our experience with Redshift and its various quirks. We tried a lot of different things to make it stable and scalable, and in doing so we learned a lot about Redshift and how it’s different from Postgres. At first, the sync process we designed was too slow to be viable for large customers. With Heap SQL, we’re syncing large amounts of data across ~80 Redshift clusters on a daily basis. Combined with Heap’s capture-everything philosophy, it enables some powerful flows: customers can define an event in our web UI, and then run arbitrary SQL on all historical instances of that event! Since so many Heap customers use Redshift, we built Heap SQL to allow them to sync their Heap datasets to their own Redshift clusters. Many companies use it, because it has made data warehousing viable for smaller companies with a limited budget.
Amazon Redshift is a data warehouse that’s orders of magnitudes cheaper than traditional alternatives.
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